Improving the accuracy of aquatic macroinvertebrate trait assignment — the importance of taxon-weighting
Bibliographic record
Abstract
Weighting family-level trait descriptions by taxon frequencies improved the accuracy of macroinvertebrate traits defined at both the individual and community level compared with trait descriptions derived from the proportionate inclusion of all taxa. Applying a priori data on (i) national rarity (rarity model) and (ii) frequency occurrence at reference sites (quality model), the deviation of family trait descriptions was measured in relation to a nonweighted null model. Overall, the quality model was more divergent than the rarity model, reflecting the respective specific and general survey protocols associated with their source data. Assessing performance with contrasting datasets demonstrated that taxon-weighting provided improved accuracy even when survey protocols associated with model data and test sites were discordant. However, accuracy generally increased as the geographic coverage and sampling methods of weighted models and test data corresponded more closely. Measuring trait variability within families provides a useful indicator of the expected accuracy of generalized trait descriptions, while distinguishing variability in terms of even or skewed dispersion suggests options for the further refinement of assessment protocols.
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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 | 0.045 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".