The influence of taxonomic level on the performance of a predictive model for water quality assessment
Bibliographic record
Abstract
Predictive models developed to assess water quality in the Mondego River basin (Portugal), based on the BEnthic Assessment of SedimenT (BEAST) model, were compared at three identifications levels: order, family, and genus (species) of macroinvertebrates. Fifty-five reference sites were originally selected for building the model, but this number was reduced to 51 (lowest level), 52 (family), and 53 (order), after the grouping procedures (CLUSTER, MDS, and SIMPER; Primer 5.2.6, Primer-E Ltd., Plymouth, UK). The discriminating variables (stepwise discriminant analysis) stream order, current velocity, pool quality, and substrate quality were common to the genus (species) and family models. Substrate quality was the only discriminating variable of the order model. The model performances, based on their ability to correctly predict reference site membership (complete MDS with jackknifed cross-validation), ranged from 78% (lowest level) to 81% (family and order levels). Twenty test sites were used to compare site assessments using each of the models. We concluded that the lowest-level model of identification provides the best evaluations of water quality assessment and performed well, that the family-level model reacted similarly and could be a good alternative for bioassessment programmes, and that a greater effort toward improving our knowledge of aquatic macroinvertebrates in Portugal is recommended as species are important in assessing water quality.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".