Corrigendum to ‘Conservation through co-occurrence: Woodland caribou as a focal species for boreal biodiversity [Biol. Conserv. 232 (2019) 238–252]’
Bibliographic record
Abstract
To populate the species list in Appendix A, we relied on existing databases of species distributions from published studies so we could build on the existing surrogacy-related findings and ensure our results were comparable with previous research.Regrettably, we neglected to update the species list with the most current classification system of bird and mammal names.We provide here an updated Appendix A that corrects incorrect taxa and outdated common names.This correction does not affect the study conclusions.We acknowledge the responsibility of the scholarly community to be inclusive and reflective of cultural diversity in naming species (Pillon 2021).We apologise for the inconvenience and thank the reader who flagged this issue for the opportunity to correct the record.Pillon, Y., 2021.The inequity of species names: The flora of New Caledonia as a case study.Biological Conservation, 253, p.108934.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.069 | 0.046 |
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".