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Record W1994736492 · doi:10.1139/f01-107

Nutrient-dependent recovery of Atlantic salmon streams from a catastrophic flood

2001· article· en· W1994736492 on OpenAlexfundvenueaboutno aff
Zhongyan Weng, Nandita Mookerji, Asit Mazumder

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeriphytonFontinalisSalmoSalvelinusNutrientSTREAMSBenthic zoneBiotaEcologyTroutEnvironmental scienceBiomass (ecology)InvertebrateStream restorationFlood mythJuvenileFisheryBiologyHabitatFish <Actinopterygii>Geography

Abstract

fetched live from OpenAlex

The impact of a major flood and recovery of the stream communities under contrasting nutrient conditions were studied in two second-order streams of Sainte-Marguerite River, Quebec. A massive flood during the summer of 1996 caused extensive displacement of the stream substratum and severe reductions in the abundance of all biota: periphyton, benthic invertebrates, juvenile Atlantic salmon (Salmo salar), and brook trout (Salvelinus fontinalis) (especially in the 0+ age-class). In the postflood recovery phase, nutrient-enriched sections recovered significantly more rapidly than the nonenriched sections. After 1 month, periphyton biomass in the enriched sections had increased to five times that of the preflood levels, and after 2 months, the invertebrate communities had recovered to preflood levels. Fish densities and growth rates also returned to normal levels more rapidly in the enriched sections. Our results suggest that nutrient-rich systems are more resilient to massive disturbances, and so, nutrient enrichment may represent a viable tool for restoring nutrient-limited systems.

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)0.001
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 score0.779
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.190
Teacher spread0.179 · 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

Citations18
Published2001
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→