Genotypic and geographic meta-information for canids and prey, to examine competitive threats facing eastern wolves (Canis lycaon)
Bibliographic record
Abstract
These .csv files contain the genotypic and geographic information for wolf-like canids in south-eastern Canada that were analyzed to examine the competitive forces facing eastern wolves (Canis lycaon). 12 scored autosomal diploid microsatellite markers can be found in the last 24 columns of the wolf .csv. Geographic information for all eastern wolves and their hybrids are restricted due to their conservation status, but are available upon request to the authors (heritabilities@gmail.com). Q-value ancestry proportions according to K=3 structure analysis, and their subsequent canid group delineation (Q>0.8) can be found as well. For prey, this data was retrieved directly from the Global Biodiversity Information Facility https://www.gbif.org/ Within the related manuscript, all data was thinned to 20 km to reduce spatial autocorrelation and opportunistic sampling bias. DOIs for GBIF data: GBIF, 2019. GBIF Occurrence Download. White-tailed deer: http://doi.org/10.15468/dl.s2xinb; Caribou: http://doi.org/10.15468/dl.cjdh0g; Moose: http://doi.org/10.15468/dl.exbtq0 [WWW Document]. URL GBIF.org (accessed 6.14.19).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.321 | 0.132 |
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".