Effects of biotic assemblage, classification, and assessment method on bioassessment performance
Bibliographic record
Abstract
Biomonitoring requires thorough evaluation of methods used to detect impairment. Using a data set of 202 reference sites and 66 test sites from the Fraser River, British Columbia, Canada, we analyzed the effects of assemblage (benthic macroinvertebrates and periphyton) and reference site classification (ecoregion, stream order, null models, and biotic groups) on two bioassessment methods (BEAST (BEnthic Assessment of SedimenT) and RIVPACS (River InVertebrate Prediction And Classification Scheme)). Although largely undisturbed, the Fraser River is affected in some areas by logging, mining, agriculture, pulp mill effluent, and urban land use. Overall performance was evaluated using the harmonic mean of precision, accuracy, and two measures of sensitivity. Invertebrates and periphyton were equally accurate and precise, but invertebrates were more sensitive. Biotic groups were the least accurate and precise classification, but also the most sensitive and had the greatest overall performance. BEAST was slightly less accurate and precise than RIVPACS, equally sensitive to simulated disturbance, and more sensitive to real disturbance. Assessments with higher sensitivity frequently had lower accuracy, indicating a possible trade-off among these aspects of performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".