Midge (<scp>C</scp>hironomidae, <scp>C</scp>haoboridae, <scp>C</scp>eratopogonidae) assemblages and their relationship with biological and physicochemical variables in shallow, polymictic lakes
Bibliographic record
Abstract
Summary We explored the relationship of aquatic midge assemblages with physicochemical and biological environmental gradients to assess which variables may govern the distribution of midge larvae in 47 shallow, polymictic lakes across N ew J ersey and N ew Y ork S tate ( NJ / NY ) in the United States of America. Subfossil taxa collected from surficial sediments (0–1 cm sediment depth), comprising 50 taxa from 47 lakes, were analysed in conjunction with environmental variables using multivariate statistical techniques. Alkalinity, maximum depth, surface area, total phosphorus and pH were identified as significant and ecologically relevant variables that explained the most variation in the midge assemblage across NJ / NY . Lake trophic state was the main driver for midge distributions in NJ / NY lakes. Biological gradients, such as per cent macrophyte cover or algal productivity (as chlorophyll a concentration), did not explain a significant portion of the variation in midge community composition. The addition of chaoborid larvae to ordinations strengthened the relationship between midge community structure and bottom oxygen concentration in NJ / NY lakes. Our results confirm that complex species–environment relationships in shallow, polymictic lakes create challenges for assessing midge assemblages along a particular environmental gradient, independently of other environmental conditions. However, it may still be possible to develop palaeolimnological inference models using midge remains to assess general historical patterns of disturbance for NJ / NY and other polymictic lakes, provided it is understood that midge‐based inferences integrate some covariation in changes along several environmental gradients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".