Modelling trophic recovery, interactions, and food web dynamics across smelter-damaged lakes
Bibliographic record
Abstract
Unstable and simplified freshwater food webs threaten the long-term potential of Canadian fisheries due to lack of resiliency in response to stressors including pollutants, climate change, and invasive species. This study utilizes lakes in Sudbury, Ontario to highlight potential drivers and limiting factors of trophic recovery from acidification and heavy-metal pollution from historical nickel smelting emissions. Three lakes across the smelter-impact gradient were selected: one severely damaged lake with a barren watershed (Baby Lake), one severely damaged lake that received sub-watershed liming treatment but retained partial forest cover (Daisy Lake), and one minimally impacted lake with intact forest but had previously been limed to enhance a fish population (Nelson Lake). Two reference lakes far from Sudbury impacts were selected for comparison. Twenty Sudbury region lakes were examined to contextualize fish community assemblages and size data across the smelter deposition zone. Stable isotope ratios of carbon (δ 13C) and nitrogen (δ 15N) were quantified in yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieu), and baseline organisms to develop quantitative population metrics and describe dietary niche partitioning in each study lake. The barren watershed lake had lowest trophic positioning, smallest body size and niche area, and greatest niche overlap among fish species. The semi-barren and forested watershed lakes were more similar to reference lakes than barren lake in isotopic metrics, signifying significant trophic recovery; however elevated niche overlap revealed additional recovery in these lakes is ongoing. Including stable isotopes in recovering lake studies provides ecosystem insights overlooked by traditional biomonitoring approaches that are critical in understanding freshwater food web responses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".