Ecology Needs a Convention of Nomenclature
Bibliographic record
Abstract
Many areas of science have adopted nomenclature rules that facilitate research and communication.In contrast, ecological terminology is constantly redefined across disciplines, plagued with synonymy and polysemy, and foundational terms (and the theories and hypotheses behind them) are overlooked.We contend that this situation handicaps the progress of ecology.We review the causes and consequences of terminological uncertainty and propose a convention of ecological nomenclature (CEN) as an indispensable requirement of ecological synthesis.The core components of a CEN are its endorsement by a transnational institution; a policy framework managed by an advisory committee; and a centralized, peer-reviewed revision of terminology whereby ecologists are proponents and users of a unique, open-access repository of terms, definitions, and ontologies.A CEN should become the basis of a cross-disciplinary platform of communication among ecologists, journals, and the public and aligns with the ongoing initiative toward data globalization in ecology and other disciplines.
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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 | 0.124 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.017 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".