1km resolution rasters of grizzly bear skull length (mm)
Bibliographic record
Abstract
Maps of grizzly bear skull size developed based on the data in the British Columbia's Compulsory Inspection database (url: https://catalogue.data.gov.bc.ca/dataset/45711667-7878-4d86-8b5e-d0be5997dd16). NAD83/BC Albers projectiong (EPSG: 3005). 1km resolution. We considered the skull length (in millimetres) of all male bears in the database over the age of seven (n = 3451) and female bears above the age of 5 (n = 5871), ages by which they are thought to have reached their full size (Mowat and Heard 2006, doi.org/10.1139/z06-016). Using these data, we generated predicted maps of male and female skull size for the province by interpolating measured skull length using a simple kriging process based on a Gaussian variogram model with the R package gstat (Pebesma 2004, doi.org/10.1016/j.cageo.2004.03.012), generating a raster with 1km resolution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".