Bibliographic record
Abstract
A comprehensive Workers Compensation Board of Alberta claims database for the plywood manufacturing industry group describing the 831 injury claims occurring from 1997 to 2002 was analyzed. The results of the study are reported with respect to: (1) claims trends in nature of injury, type of accident or exposure, source of injury, and body part injured, (2) the effect of work experience and worker age on the above classifications, (3) the impact of observed claims trends in terms of cost and duration of claim, (4) the relationship of Alberta figures to overall incidence rates of non-fatal injuries/illnesses generally, and the characteristics of those injuries/illnesses specifically, reported by the Bureau of Labor Statistics. The majority of successful claims were experienced by males 35–44 years of age with 2–5 years of work experience. Musculoskeletal injuries were the most common nature of injury (52%) and the upper extremity was the most frequently injured region of the body (41.8%). The majority of claims were classified as medical aid (64.8%) and did not result in time loss. Comparison of the incidence rates of specific injury/illness characteristic categories observed in Alberta versus those reported by the Bureau of Labor Statistics indicate that the survey of occupational injuries and illness is not an accurate indication of the characteristics of accepted compensation claims in the plywood industry group of Alberta, Canada.The description of injury trends within the plywood manufacturing industry stands to positively impact the effectiveness of prevention and rehabilitation programs addressing workplace injuries and illnesses. In addition, comparison of incidence rates observed in Alberta to those estimated by the Survey of Occupational Injuries and Illnesses provides insight into the differences between the respective statistical sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".