--- NATIONAL WAGES ---
  total_employment : 1,700
  annual_median    : $50,840
  annual_pct10     : $36,380
  annual_pct25     : $41,900
  annual_pct75     : $61,450
  annual_pct90     : $69,350
  annual_mean      : $52,910
  hourly_median    : $24.44

--- GEOGRAPHIC DISPERSION ---
  highest_state    : Louisiana ($80,930)
  lowest_state     : Georgia ($34,870)
  dispersion_ratio : 2.321x

--- TOP STATES BY WAGE (10 total) ---
  Agriculture, Forestry, Fishing and Hunting emp:    1,420  median: $  52,830
  Manufacturing                            emp:      160  median: $  41,900

--- TOP INDUSTRIES BY EMPLOYMENT (2 total) ---
  Agriculture, Forestry, Fishing and Hunting emp:    1,420  median: $  52,830
  Manufacturing                            emp:      160  median: $  41,900

exact_match_status : no_exact_match
matched_title      : N/A
match_score        : 0.0000
wikidata_qid       : N/A
word_count         : 0
wikipedia_url      : N/A
license            : CC BY-SA 4.0
fetched_at         : 2026-06-02T22:36:37.335927Z

--- SEMANTIC NEIGHBORS (5) ---

  Title: Logging (similarity: 0.4242)
  URL: https://en.wikipedia.org/wiki/Logging
  QID: Q845249
  Extract: Logging is the process of cutting, processing, and moving trees to a location for transport. It may include skidding, on-site processing, and loading of trees or logs onto trucks or skeleton cars. In forestry, the term logging is sometimes used narrowly to describe the logistics of moving wood from 

  Title: Gyppo logger (similarity: 0.3158)
  URL: https://en.wikipedia.org/wiki/Gyppo_logger
  QID: Q16983015
  Extract: A gyppo or gypo logger is a logger who runs or works for a small-scale logging operation that is independent from an established sawmill or lumber company. The gyppo system is one of two main patterns of historical organization of logging labor in the Pacific Northwest United States, the other being

  Title: Illegal logging in Madagascar (similarity: 0.3273)
  URL: https://en.wikipedia.org/wiki/Illegal_logging_in_Madagascar
  QID: Q3062135
  Extract: Illegal logging has been a problem in Madagascar for decades and is perpetuated by extreme poverty and government corruption. Often taking the form of selective logging, the trade has been driven by high international demand for expensive, fine-grained lumber such as rosewood and ebony. Historically

  Title: Log flume (similarity: 0.2857)
  URL: https://en.wikipedia.org/wiki/Log_flume
  QID: Q1425792
  Extract: A log flume or lumber flume is a watertight flume constructed to transport lumber and logs down mountainous terrain using flowing water. Flumes replaced horse- or oxen-drawn carriages on dangerous mountain trails in the late 19th century. Logging operations preferred flumes whenever a reliable sourc

  Title: The Other World's Books Depend on the Bean Counter (similarity: 0.1842)
  URL: https://en.wikipedia.org/wiki/The_Other_World's_Books_Depend_on_the_Bean_Counter
  QID: Q130748785
  Extract: The Other World's Books Depend on the Bean Counter  is a Japanese light novel series written by Yatsuki Wakatsu and illustrated by Kikka Ohashi. It was serialized online from March to December 2018 on the user-generated novel publishing website Shōsetsuka ni Narō. It was later acquired by Enterbrain

model_pass1          : claude-sonnet-4-20250514
model_pass2          : claude-haiku-4-5-20251001
inference_confidence : medium
confidence_notes     : Limited detailed task and skill data due to catchall nature of occupation, but strong industry context and job title variety provide reasonable foundation for inference.
inferred_at          : 2026-06-03T23:08:56.355062+00:00
tokens_input         : 2,223
tokens_output        : 3,275
cost_usd             : $0.033387
wikipedia_used       : False
wikipedia_title      : None
wikipedia_note       : No exact Wikipedia match exists, though general logging content provides industry context. The specialized nature of this catchall occupation makes it distinct from broader logging roles.

--- PROSE FIELDS ---

ROLE SUMMARY:
Logging Workers, All Other encompasses specialized forestry workers who perform cutting, processing, and moving operations not covered by other specific logging occupations. These workers operate equipment like air saws and bark peelers while supporting timber harvesting operations in forests and lumber facilities. They form a critical component of the wood products supply chain, handling diverse tasks from tree cutting to bark removal.

DAY IN THE LIFE:
Workers operate specialized equipment including air saws for precision cutting, bark peelers to remove tree bark, and boom equipment for moving logs in water. They scale and measure timber, cut logs into specific lengths called billets or bolts, and prepare wood materials for transport. Daily tasks include equipment maintenance, safety inspections, and coordinating with other logging crew members. Workers may blaze trails through forests, operate barking machinery in processing facilities, or work on log booms guiding timber down waterways.

WHO THRIVES:
Successful logging workers possess strong physical stamina and mechanical aptitude to operate heavy forestry equipment safely. They demonstrate spatial reasoning skills for estimating tree fall patterns and log dimensions, plus hand-eye coordination for precision cutting operations. Workers thrive with outdoor work preferences, comfort in remote forest environments, and ability to work independently with minimal supervision. Risk tolerance and safety consciousness are essential given the hazardous nature of forestry operations involving heavy machinery and falling trees.

CAREER ENTRY:
Most positions require a high school diploma or equivalent, with extensive on-the-job training provided by experienced loggers or logging companies. Workers typically start in entry-level positions learning equipment operation and safety procedures over 6-12 months. Some employers prefer candidates with vocational training in forestry, heavy equipment operation, or related mechanical fields. Physical fitness and ability to work in outdoor conditions year-round are essential requirements.

CAREER TRAJECTORY:
Experienced workers can advance to crew supervisor or logging foreman roles, overseeing multiple workers and coordinating harvesting operations. Some transition to equipment maintenance specialist positions or start independent logging operations as gyppo loggers. Others move into related forestry careers such as timber buyer, forestry technician, or heavy equipment operator in construction industries. Career progression often leads to management roles in lumber mills or forestry companies.

MARKET INTELLIGENCE:
With 1,700 workers nationally earning a median of $50,840 annually according to BLS OEWS May 2025, this is a small but specialized occupation. Geographic wage variation is significant, with Louisiana workers earning $80,930 compared to $34,870 in Georgia, reflecting regional timber industry strength. Employment concentrates heavily in Agriculture, Forestry, Fishing and Hunting (1,420 workers) with secondary presence in Manufacturing (160 workers). The occupation serves niche roles supporting the broader logging industry, with demand tied to construction activity and lumber markets.

AUTOMATION OUTLOOK:
Physical nature of forestry work and need for human judgment in irregular forest environments provide moderate protection from automation. While some equipment becomes more sophisticated with GPS and computerized controls, the varied terrain and unpredictable conditions of logging operations still require human operators for safe and effective timber harvesting.

--- REASONED EDGES ---
  [career_pathway] Fallers (45-4021.00) — confidence:high
    reasoning: Both work in logging operations with similar outdoor forest environments and equipment usage.
    data: shared forestry industry context
    data: similar physical demands
    data: overlapping job titles
  [task_similarity] Logging Equipment Operators (45-4022.00) — confidence:high
    reasoning: Job titles like Air Saw Operator and Boom Worker indicate equipment operation overlap.
    data: air saw operator title
    data: boom equipment operation
    data: logging industry concentration
  [skill_overlap] Log Graders and Scalers (45-4023.00) — confidence:medium
    reasoning: Bark Scaler job title indicates measurement and grading skill overlap.
    data: bark scaler job title
    data: timber scaling activities
  [transferable_skill] Industrial Truck and Tractor Operators (53-7051.00) — confidence:medium
    reasoning: Heavy equipment operation skills transfer across industries as evidenced by manufacturing employment.
    data: manufacturing industry employment
    data: equipment operator titles
    data: machinery operation skills
  [knowledge_overlap] Mobile Heavy Equipment Mechanics (49-3042.00) — confidence:medium
    reasoning: Equipment maintenance knowledge needed for specialized logging machinery operation.
    data: specialized equipment operation
    data: machinery maintenance requirements

--- NORMALIZER SIGNALS ---
  match_keywords   : ['logging', 'logger', 'timber', 'forestry', 'bark', 'sawyer', 'cutter', 'boom']
  exclude_keywords : ['office', 'administrative', 'sales', 'management', 'supervisor']
  title_patterns   : ['*logger*', '*cutter*', '*operator*', '*peeler*', '*scaler*']
  common_variations: ['all-round logger', 'air saw operator', 'bark peeler', 'boom worker', 'bolt cutter', 'barker', 'billet cutter', 'boom man']

total_terms    : 40
top_words      : ['logging', 'forestry', 'equipment', 'lumber', 'operations', 'timber', 'cutting', 'logs', 'heavy', 'operate', 'processing', 'moving', 'trees', 'wood', 'log', 'specialized', 'bark', 'transport', 'forest', 'gyppo']
source_layers  : onet_tasks | onet_dimensions | dwas | wikipedia | inference

TERM                    COUNT     FREQ  DOMINANT SOURCE      SOURCE BREAKDOWN
──────────────────────────────────────────────────────────────────────────────────────────
logging                    21  0.03791  inference            inference:52%  wikipedia:48%
forestry                   11  0.01986  inference            inference:82%  wikipedia:18%
equipment                  10  0.01805  inference            inference:100%
lumber                      8  0.01444  wikipedia            wikipedia:62%  inference:38%
operations                  8  0.01444  inference            inference:88%  wikipedia:12%
timber                      7  0.01264  inference            inference:86%  wikipedia:14%
cutting                     5  0.00903  inference            inference:80%  wikipedia:20%
logs                        5  0.00903  wikipedia            wikipedia:60%  inference:40%
heavy                       5  0.00903  inference            inference:80%  wikipedia:20%
operate                     5  0.00903  inference            inference:80%  wikipedia:20%
processing                  4  0.00722  wikipedia            wikipedia:50%  inference:50%
moving                      4  0.00722  wikipedia            wikipedia:50%  inference:50%
trees                       4  0.00722  wikipedia            wikipedia:75%  inference:25%
wood                        4  0.00722  wikipedia            wikipedia:50%  inference:50%
log                         4  0.00722  wikipedia            wikipedia:50%  inference:50%
specialized                 4  0.00722  inference            inference:100%
bark                        4  0.00722  inference            inference:100%
transport                   3  0.00541  wikipedia            wikipedia:67%  inference:33%
forest                      3  0.00541  inference            inference:67%  wikipedia:33%
gyppo                       3  0.00541  wikipedia            wikipedia:67%  inference:33%
logger                      3  0.00541  wikipedia            wikipedia:100%
operation                   3  0.00541  inference            inference:67%  wikipedia:33%
madagascar                  3  0.00541  wikipedia            wikipedia:100%
government                  3  0.00541  wikipedia            wikipedia:100%
flume                       3  0.00541  wikipedia            wikipedia:100%
water                       3  0.00541  wikipedia            wikipedia:67%  inference:33%
flumes                      3  0.00541  wikipedia            wikipedia:100%
march                       3  0.00541  wikipedia            wikipedia:100%
harvesting                  3  0.00541  inference            inference:100%
tree                        3  0.00541  inference            inference:100%
safety                      3  0.00541  inference            inference:100%
physical                    3  0.00541  inference            inference:100%
nature                      3  0.00541  inference            inference:100%
include                     2  0.00361  wikipedia            wikipedia:50%  inference:50%
term                        2  0.00361  wikipedia            wikipedia:100%
sawmill                     2  0.00361  wikipedia            wikipedia:100%
small                       2  0.00361  wikipedia            wikipedia:50%  inference:50%
independent                 2  0.00361  wikipedia            wikipedia:50%  inference:50%
patterns                    2  0.00361  wikipedia            wikipedia:50%  inference:50%
trade                       2  0.00361  wikipedia            wikipedia:100%