Effects of atrazine and chlorothalonil on the reproductive success, development, and growth of early life stage sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
The effects of 2 currently used commercial pesticide formulations on Pacific sockeye salmon (Oncorhynchus nerka), from fertilization to emergence, were evaluated in a gravel-bed flume incubator that simulated a natural streambed. Embryos were exposed to atrazine at 25 µg/L (low atrazine) or atrazine at 250 µg/L (high atrazine) active ingredient (a.i.), and chlorothalonil at 0.5 µg/L (low chlorothalonil) or chlorothalonil at 5 µg/L a.i. (high chlorothalonil) and examined for effects on developmental success and timing, as well as physical and biochemical growth parameters. Survival to hatch was reduced in the high chlorothalonil group (55% compared with 83% in controls), accompanied by a 24% increase in finfold deformity incidence. Reduced alevin condition factor (2.9-5.4%) at emergence and elevated triglyceride levels were seen in chlorothalonil-exposed fish. Atrazine exposure caused premature hatch (average high atrazine time to 50% hatch [H50] = 100 d postfertilization [dpf]), and chlorothalonil exposure caused delayed hatch (high chlorothalonil H50 = 108 dpf; controls H50 = 102 dpf). All treatments caused premature emergence (average time to 50% emergence [E50]: control E50 = 181 dpf, low chlorothalonil E50 = 175 dpf, high chlorothalonil E50 = 174 dpf, high atrazine E50 = 175 dpf, low atrazine E50 = 174 dpf), highlighting the importance of using a gravel-bed incubator to examine this subtle, but critical endpoint. These alterations indicate that atrazine and chlorothalonil could affect survival of early life stages of sockeye salmon in the wild. Environ Toxicol Chem 2017;36:1354-1364. © 2017 SETAC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".