Bioassessment of stream ecosystems enduring a decade of simulated degradation: lessons for the real world
Bibliographic record
Abstract
The effects on benthic macroinvertebrate communities of simulated degradation of streams enabled evaluation of the effects of starting condition, type of degradation, and biota descriptor on the type 1 and type 2 error rates of bioassessment. Benthic macroinvertebrate communities from five reference streams in the Fraser River basin (British Columbia, Canada) were used as the starting conditions of replicated simulations of the effects of suspended sediments in three temporal patterns (none, one-time severe, constant moderate). The dynamics of the simulated stream communities and the type 1 and type 2 errors associated with bioassessments, as described by (i) taxon richness, (ii) EPT (Ephemeroptera, Plecoptera, Trichoptera) richness, (iii) proportion of EPT individuals in the community, (iv) difference in composition from the median reference community (MCDist), (v) Simpson’s diversity, and (vi) Simpson’s equitability, depended on the combination of starting condition, simpact treatment, and the biota descriptor. To reduce type 1 and type 2 errors in bioassessments using the reference condition approach, bioassessment programs should include (i) matching of test and expected reference communities and refinement of the definition of reference condition and (ii) several biota descriptors that include measures of richness, tolerance, and community composition.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".