Sedimentary Midges as Paleoindicators of Deep-water Oxygen Conditions Across a Broad Trophic Gradient in Boreal Lakes
Bibliographic record
Abstract
Cultural eutrophication, the addition of excess nutrients to an aquatic system, is a significant water quality concern that often promotes excess algal growth and deep-water oxygen depletion. Deep-water oxygen also influences internal nutrient loading and is an important parameter used to assess cold-water fish habitat, though long-term data are often unavailable. Chironomid (Diptera: Chironomidae) assemblages have been shown to change with deep-water oxygen concentrations and can therefore be used to reconstruct these missing data sets. This thesis used paleolimnological techniques to analyze inferred whole-lake primary production, sedimentary chironomid assemblages, and inferred volume-weighted hypolimnetic oxygen to determine how cold-water fish habitat has changed through time. I will also examine whether biological recovery after nutrient-targeting remediation was introduced was evident in sedimentary chironomid assemblages. I focused on two lakes with increasing inferred whole-lake primary production (Muskrat and Stoco lakes, Ontario) and one lake with decreasing inferred primary production (Lac Duhamel, near Mont Tremblant, Québec) over time. The majority of change in response to elevated inferred whole-lake primary production is evident in littoral taxa and head capsule concentrations, though oxy-conforming profundal taxa (e.g. Micropsectra) did respond to increased whole-lake primary production. Overall, deep-water oxygen recovery after nutrient-targeting remediation was not evident in the sedimentary chironomid assemblages and that there were generally only subtle responses to elevated whole-lake primary production. Many of our lakes had historically low deep-water oxygen concentrations that were suboptimal for cold-water fish throughout their sedimentary records, with two lakes experiencing modest declines after there were increases in whole-lake primary production. These paleolimnological data can be used to set realistic mitigation targets for deep-water oxygen conditions and cold-water fish habitat restoration.
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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 | 0.001 |
| 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".