Factors influencing the distribution of chironomids in lakes distributed along a latitudinal gradient in northwestern Quebec, Canada
Bibliographic record
Abstract
The effect of 33 environmental variables on the distribution of chironomids was studied in 60 lakes of northwestern Quebec (Canada). A detrended canonical correspondence analysis detected linearity in the chironomid assemblages, thus redundancy analysis was used to identify the variables affecting the chironomid communities. Lake depth, dissolved organic carbon (DOC), mean August air temperature, and surface water temperature were the four variables that best explained the distribution of chironomids. Partial least squares analysis was used to develop new inference models. Among models, the one for mean August air temperature had the highest coefficient of determination (r[Formula: see text] = 0.67) and the lowest root mean square error of prediction (= 1.17 °C). The results indicated that for downcore temperature reconstructions, it might be hard to dissociate the combined effects of temperature, DOC, and depth. Changes in taxa such as Heterotrissocladius brundini-type, Heterotrissocladius subpilosus-type, Heterotrissocladius grimshawi-type, Micropsectra radialis-type, Tanytarsus lugens-type, and Microtendipes can be attributed to changes in lake depth and (or) temperature. Changes in Heterotanytarsus, Dicrotendipes, Cryptotendipes, and Cryptochironomus might be attributed to shifts in temperature and DOC. Relationships among temperature, DOC, and lake depth should be studied in a "neo-ecology" design to better understand their impact on chironomid assemblage composition.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".