Are different benthic communities in Arctic delta lakes distinguishable along a hydrological connectivity gradient using a rapid bioassessment approach?
Bibliographic record
Abstract
Aquatic habitats in the Canadian Arctic are expected to come under increasing stress due to projected effects of climate change. There is a need for community-based biomonitoring programs to observe and understand the effects of these stressors on the environment. Here we present results from a 5 year annual sampling program of benthic invertebrates from lakes in the Mackenzie Delta, Northwest Territories, using a rapid bioassessment protocol. Connectivity between the deltaic lakes and main channels is a major driver of lake function and is expected to be substantially impacted by climate change. Lakes were selected along a gradient of connectivity based on sill elevation above the river. Using multivariate analyses of community structure, we determined that benthic assemblages responded to differences in connection time among lakes. This response was detected using a coarse taxonomic level that could be applied by community groups or volunteers but was stronger when invertebrates were identified to the family and genus levels. A secondary gradient was observed that corresponded to productivity gradients in lakes that are isolated from the river during summer. We show that benthic assemblages have potential use as sensitive indicators of climate-mediated changes to the hydrology of lakes in the Mackenzie Delta.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".