Improved prediction of Canada lynx distribution through regional model transferability and data efficiency
Bibliographic record
Abstract
These datasets were used to generate an ensemble species distribution model using the R package Bioclim2. The file "Model_WA30VegOnly_TrainData_15Dec2020" contains the used and background locations used to make the best-performing model in the paper referenced below. It contains n=5026 actual lynx GPS locations, with the covariate values used in the analysis appended to each location. The coordinates of the points have been removed due to the sensitivity of the species. The background data were used in equal sample size to the use data, but subsampled repeatedly from this full dataset for use as multiple instances of psuedo-absences. The file "Moel_WA30VegOnly_TestData_15Dec2020" gives an equal number of used and background locations for use as model validation data. The data in this file were not used for model construction, but were collected on the same GPS collars, so are strongly spatially correlated with the model construction data. Further information and details on the datasets can be found in "Olson et al., 2021, Improved Prediction of Canada lynx distribution through regional model transferability and data efficiency, Ecology and Evolution".
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".