Water temperature variability in the St. Lawrence river near Montreal
Bibliographic record
Abstract
Information on water temperature is essential to the interpretation of biological data. The objectives of this study were to characterize the variability of St. Lawrence River water temperatures and to examine the linkages between temperature and flow regime as a result of the confluence of the St. Lawrence and Ottawa rivers in the Montreal area. Transversal differences in conductivity and temperature were observed year-round at a cross-section of the river located downstream of the confluence of the St. Lawrence (flowing along the south shore) and Ottawa rivers (flowing along the north shore). From April to July, St. Lawrence River waters originating from Lake Ontario were up to 3.5°C colder than those coming from the Ottawa River. During the first two weeks of August, the two water masses were within 0.5°C of each other. From mid-August to the end of March, however, waters from Lake Ontario were systematically 2 to 3.5°C warmer than water from the Ottawa River. This pattern likely resulted from the considerably large volume of water originating from Lake Ontario, which is slow to warm up in spring and summer and slow to release stored heat through the fall and winter. Water hardness values from five water filtration plants in the Montreal area revealed their exposure to different water masses, even though some of the intakes were in close proximity. Water hardness values were generally high at the Charles-J. Des Baillets (DB), Atwater (AW) and Longueuil (LO) plants, corresponding to the predominant, year-round influence of waters originating from Lake Ontario. In contrast, the John Labatt (JL) and Pointe Claire (PC) plants were periodically exposed to Ottawa River waters, as shown by lower and seasonally-variable hardness values. The water temperature at each plant was consistent with the seasonal pattern of exposure to either water mass. Multiple regression models (r² = 0.89) predicting daily water temperature based on environmental variables were developed for the three plants with intakes in waters originating from Lake Ontario (LO, DB and AW). Models included air temperature, season, Ottawa river discharge and the ratio of Ottawa to total river discharge. Water temperature did not differ significantly between LO and DB; temperature recorded at both plants was warmer (by 0.6°C) than at the AW plant. Over the years, mean annual water temperature increased at three of the plants, showing rates of 0.5°C (JL, 1978-2001), 0.7°C (DB, 1981-2001) and 1.2°C (LO, 1992-2002) per 10-year period. No long-term trend in water temperature was detected at AW (1919-2001), owing to the presence of alternating warm and cold water years. However, none of the 10 coldest years and 4 of the 9 warmest years of the series occurred since 1980. For terrestrial plants (air temperature), no significant temporal trends were observed in the dates of the beginning, end or duration of the growing season. For aquatic organisms, growing season calculated for 5, 10, 15 and 20°C thresholds generally indicated a later water cooling in the fall and an increase in the duration of the growing season. The average annual water temperature at the AW plant was negatively related (p 1°C increase in mean monthly temperature for each 1-m decrease in average level (DB, JL). Years of low discharge coincided with a precocious warm-up in the spring and a late cooling in the fall, yielding a higher cumulative number of degree-days and a longer growing season.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".