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Record W2887763225

Water temperature variability in the St. Lawrence river near Montreal

2003· article· en· W2887763225 on OpenAlexaboutno aff
Christiane Hudon, Alain Patoine, Alain Armellin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsShoreHydrology (agriculture)Spring (device)Environmental scienceConfluenceAir temperatureWater levelOceanographyGeographyGeologyClimatology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.185
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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