Trait patterns of aquatic insects across gradients of flow-related factors: a multivariate analysis of Canadian national data
Bibliographic record
Abstract
Large-scale comparisons of aquatic bioassessment metrics based on taxonomic composition are currently constrained by the biogeographic limitations of taxon occurrence. The use of species trait patterns offers a possibility to overcome this constraint. We examine the assertion that the trait composition of aquatic insect communities changes in a consistent manner along similar environmental disturbance gradients by evaluating relationships between traits and three flow-related variables (velocity, water temperature, and dissolved oxygen) in 13 independently collected Canadian data sets. Certain trait states such as low crawling rate, common occurrence in drift, short adult life span, erosional rheophily, medium size at maturity, and cold or cool thermal preference were consistently sensitive to all three flow-related factors, velocity in particular, despite biogeographic differences in faunal composition. Trait modalities exhibiting the highest mean correlation with velocity, temperature, and dissolved oxygen were identified, and the potential confounding effect of trait “syndromes” was addressed by restricting the selection of flow-sensitive traits to those with high evolutionary lability. The results of the study provide a basis for the future development of flow bioassessment metrics at the national Canadian scale and potentially at the international scale.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".