Frequency Analysis as a Tool for Assessing Adverse Conditions During a Massive Fish Kill in the St. Lawrence River, Canada
Bibliographic record
Abstract
Abstract During the summer of 2001, the largest fish kill in the recorded history of the St. Lawrence River occurred. More than 25,000 dead carp were recovered. Preliminary analyses suggested hydroclimatic factors may have triggered the fish kill. Long time series of hydroclimatic variables were available upstream and downstream from the study area. In order to investigate if hydroclimatic conditions were extreme during the summer of 2001, frequency analyses were performed on time series of air and water temperature, water level, and solar radiation. During this period, the daily maximum water temperature was abnormally high (return period of 47 years) relative to other years. Air temperature was also high (return period of 22 years) and water level was very low (return period of 67 years). Results showed that hydroclimatic forcings were acting at two different time scales. First, short-term extremes are more likely to have direct impacts on ecosystems, such as lethal stress caused by oxygen depletion in shallow areas. Long-term extremes have indirect effects, which are more difficult to detect, such as immunosuppression. These results reiterate the importance of water temperature in aquatic habitat, particularly in the present context of global warming and climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".