Secondhand effects of alcohol use among university students: computerised survey
Bibliographic record
Abstract
ABSTRACT Environmental impact assessments often rely on best available information, which may include models that were not designed for purpose and are not accompanied by an assessment of limitations. We reproduced available models of boreal woodland caribou resource selection and demography and evaluated their suitability for projecting impacts of development in the Ring of Fire on boreal caribou in the Missisa range (Ontario, Canada). The specificity of the resource selection model limited usefulness for predicting impacts, and high variability in model coefficients among ranges suggests responses vary with habitat availability. The aspatial demographic model projects decreasing survival and recruitment with increasing disturbance, but high variability among populations implies the importance of these impacts depends on population status, and there is no current status estimate. New models that are designed for forecasting, informed by more current herd status information and information from neighbouring ranges, are required to better inform decisions. To demonstrate how open-source tools and reproducible workflows can improve the transparency and reusability of models we developed an R package for data preparation, resource selection, and demographic calculations. Open-source tools, reproducible workflows, and reuseable forecasting models can improve our collective ability to inform wildlife management decisions in a timely manner.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".