Examining wildland fire fighter candidate pass rates over five years post-implementation on a newly-developed physical employment standard1
Bibliographic record
Abstract
BACKGROUND: To assess the post-implementation impact of the Canadian Type 1 Wildland Fire Fighter (WFF) Fitness Test Circuit (WFX-FIT), a retrospective descriptive analysis of anonymized aggregate data collected between 2012-2016 was conducted. OBJECTIVES: The objectives were to examine the pass rates of Type 1 WFF in each fire jurisdiction and on the standard for exchanging Type 1 WFF between agencies, the interagency exchange standard, by age group and sex and to propose what other information could be of value in assessing the effectiveness of implementing a physical employment standard. METHODS: Frequencies and pass rate percentages were compared by sex and age groups (<40 years, ≥40 years). RESULTS: Between 2012-2016, pass rates for all participants on the jurisdictional and interagency exchange performance standards improved from 93.2% to 95.6% and 79.1% to 87.6%, respectively. CONCLUSIONS: We conclude that since the WFX-FIT was implemented, there has been an increase in the number of exchange-eligible Type 1 WFF for suppression of wildfires in Canada.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".