STUDY PROTOCOL Use of pedometer-driven r a a
Bibliographic record
Abstract
Full list of author information is available at the end of the articleportant sector in the New Zealand (NZ) economy, employing approximately 24,000 workers, and contrib-uting approximately 13 % of New Zealand’s exports [1]. Meat processing involves different work stages that in-clude slaughtering, boning, cutting, and packing, which demand different physical workloads and tasks. These often require prolonged periods in task-related non-neutral postures, potentially leading to an increased risk of occupational injuries. are varied, with musculoskeletal disorders (MSD) being a commonly reported health problem [2-4]. Relevant risk factors include hazardous working conditions [4,5] with repetitive movements, heavy physical workload, and sus-tained standing [6-8] that have been linked to increased levels of disability, sick leave, and work incapacity [9]. The prevalence of MSD among meat processing workers has been previously published [2]. For example meat processing in the USA is considered one of the most hazardous industries, with an overall incidence injury rate of 6.9 per 100 fulltime workers in 2009 [10], while in Canada, the incidence injury rate was 23.48 per 1,000
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.025 |
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".