Towards reconciliation of the four world bird lists: hotspots of disagreement in taxonomy of raptors
Bibliographic record
Abstract
There are currently four world bird lists referenced by different stakeholders including governments, academic journals, museums and citizen scientists. Consolidation of these lists is a conservation and research priority. In reconciling lists, care must be taken to ensure agreement in taxonomic concepts-the actual groups of individual organisms circumscribed by a given scientific epithet. Here, we compare species-level taxonomic concepts for raptors across the four lists, highlighting areas of disagreement. Of the 665 species-level raptor taxa observed at least once among the four lists, only 453 (68%) were consistent across all four lists. The Howard and Moore Checklist of the Birds of the World contains the fewest raptor species (528), whereas the International Ornithological Community World Bird List contains the most (580) and these two lists are in the most disagreement. Of the disagreements, 67% involved owls, and Indonesia was the country containing the most disagreed upon species (169). Finally, we calculated the amount of species-level agreement across lists for each avian order and found raptor orders spread throughout the rankings of agreement. Our results emphasize the need to reconcile the four world bird lists for all avian orders, highlight broad disagreements across lists and identify hotspots of disagreement for raptors, in particular.
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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 | 0.085 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".