Comparison of models predicting invertebrate assemblages for biomonitoring in the Fraser River catchment, British Columbia
Bibliographic record
Abstract
A multivariate, predictive model based on the reference-condition approach is described for the Fraser River catchment in British Columbia. Benthic invertebrate assemblages and environmental descriptors were measured at more than 200 sites from 1994 to 1996, including 219 reference sites. Reference sites were classified into groups representing similar invertebrate assemblages. Five such classifications were produced, using three taxonomic levels (family, genus, and species) and species and family multimetrics. For each of the classifications, discriminant function analysis was used, with environmental descriptors, to develop a predictive model for the reference sites. These models predicted from 43.8% (species) to 61.6% (family) of the reference sites to the correct benthic group. Each model was used to assess deviation from reference condition for 21 test sites exposed to either agriculture, logging, or mining. The models were examined with regard to their sensitivity, robustness, usability, temporal variability, predictive performance, and model certainty. The family and family-metric models were ranked best, followed by the species-metric, genus, and species models. The family-level model is recommended for assessment purposes; its overall performance was slightly superior to the family-metric model and it avoids an extra step in calculation of the metrics. However, the species-level model is recommended for conservation or biodiversity issues.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".