Implications of seasonal variation for biomonitoring with predictive models in the Fraser River catchment, British Columbia
Bibliographic record
Abstract
Reference-condition models for the Fraser River catchment were developed using samples collected during the autumn of 1994, 1995, and 1996. The goal of this study was to examine applicability to the reference-condition models of samples collected in other seasons and the effect of taxonomic resolution (genus and family) on model sensitivity to seasonal variation in the benthic invertebrate assemblage. Samples from eight streams representing interior, coastal, and large-river habitats were collected in spring, summer, autumn, and winter of 1995 and in spring of 1996. The benthic invertebrate assemblage changed seasonally such that the models could not be used for seasons other than autumn. The models were equally sensitive to seasonal variation when genus-level or family-level data were used. We recommend that test samples (i.e., samples collected from disturbed sites and meant for comparison with the reference database) be collected either during the autumn or over multiple sampling dates to reduce the possibility that seasonal shifts or stochastic events will lead to erroneous conclusions about the state of a test site.
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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".