Effect of Seasonal Changes on Predictive Model Assessments of Streams Water Quality with Macroinvertebrates
Bibliographic record
Abstract
Abstract In this study we investigate how seasonal variability in aquatic macroinvertebrate communities affects the performance of a predictive model developed to assess environmental quality. Macroinvertebrates were sampled from nine not visibly disturbed sites located in different streams of the Mondego catchment across a full year. Organisms were identified to the lowest practicable taxonomic level and their abundances recorded at three taxonomic levels (order, family and lowest level). The seasonal samples were examined with regard to seasonal variation using three predictive models at order, family and lowest taxonomic level. The models showed increasing effect of seasonal changes across taxonomic levels, from order to the lowest level. When using the current models samples should be taken in the same season as the reference sites were sampled. Furthermore, data from more reference sites should be added to the model in order to encompass sufficient natural variation and allow the use of the model in different seasons. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".