Additive Effects of Chlorinated Biocides and Water Temperature on Fish in Thermal Effluents with Emphasis on the Great Lakes
Bibliographic record
Abstract
We reviewed the literature on the effects of chlorine on selected Great Lakes fishes during the summer when chlorine is used to control biofouling in cooling systems at power generating stations. Mortalities of fish are usually not solely due to chlorine toxicity but to complex additive functions and interactions of various stressors, in particular temperature. Elevated temperature appears to be important in magnifying the effects of the toxicity of chlorine to fish. When chlorination is used at temperatures near the thermal maxima, but not sufficiently high to exclude fish, high mortality rates can be expected. Most of the fish that lose equilibrium during exposure do not survive. Fish exposed to sublethal levels of chlorine become lethargic and often gulp air and frequently suffer increased predation pressures from birds and other fish. Additionally, hematological and biochemical disturbances, and potentially irreversible gill damage, may impair the lifetime fitness of fish exposed to chlorine. The sensitivity of different species of fish to chlorine toxicity varies widely. As such, chlorination regimes should be evaluated on a daily basis to account for differences in species composition and water temperatures. Most of the chlorine exposure concentrations reported in the literature are for 50% mortality, but the highest concentration resulting in no mortality, loss of equilibrium, or sublethal effects, is a more appropriate value for management and conservation. We also advocate comprehensive ecological risk assessments to determine the scope of impact on all organisms, not just fish. Only a series of in situ and laboratory studies for each situation will provide biologically meaningful values and the basis for relevant regulations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".