Avibase – a database system for managing and organizing taxonomic concepts
Bibliographic record
Abstract
Scientific names of biological entities offer an imperfect resolution of the concepts that they are intended to represent. Often they are labels applied to entities ranging from entire populations to individual specimens representing those populations, even though such names only unambiguously identify the type specimen to which they were originally attached. Thus the real-life referents of names are constantly changing as biological circumscriptions are redefined and thereby alter the sets of individuals bearing those names. This problem is compounded by other characteristics of names that make them ambiguous identifiers of biological concepts, including emendations, homonymy and synonymy. Taxonomic concepts have been proposed as a way to address issues related to scientific names, but they have yet to receive broad recognition or implementation. Some efforts have been made towards building systems that address these issues by cataloguing and organizing taxonomic concepts, but most are still in conceptual or proof-of-concept stage. We present the on-line database Avibase as one possible approach to organizing taxonomic concepts. Avibase has been successfully used to describe and organize 844,000 species-level and 705,000 subspecies-level taxonomic concepts across every major bird taxonomic checklist of the last 125 years. The use of taxonomic concepts in place of scientific names, coupled with efficient resolution services, is a major step toward addressing some of the main deficiencies in the current practices of scientific name dissemination and use.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.027 |
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".