Macroinvertebrates on coarse woody debris in the littoral zone of a boreal lake
Bibliographic record
Abstract
Logging activity was a regular practice in the boreal forest of Quebec during the 19th century and may have had an impact on the temporal dynamics of aquatic coarse woody debris (CWD) and associated organisms. The dynamics of white cedar (Thuja occidentalis) CWD inputs from the riparian environment in a boreal lake in Eastern Quebec, Canada, over the past 350 years were reconstructed and differences in the macroinvertebate communities according to CWD age, season of sampling (spring, summer and autumn), depth and site were investigated. It was hypothesised that CWD macroinvertebrate community structure would change with CWD age, season and depth, but not among sites. No significant correlation was found between CWD age and macroinvertebrate densities and taxa number. The macroinvertebrate community was highly variable in space and time. Season was the main factor influencing taxa composition and the relative densities of individuals. The mean density was more than twofold greater in autumn than in spring and summer (1046, 1049 and 2335 individuals m–2 in spring, summer and autumn respectively). Density and taxa number decreased with depth, but site did not appear to influence the community. As CWD inputs increased during the log-driving period, impacts on macroinvertebrate communities were likely to be important and should be documented across the boreal zone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".