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Record W2146747064 · doi:10.1139/f03-114

The role of sewage in a large river food web

2003· article· en· W2146747064 on OpenAlexvenueaboutno aff
Adrian M.H. deBruyn, David J. Marcogliese, Joseph B. Rasmussen

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSewageOutfallEnvironmental scienceFood webPlumeLittoral zoneEnvironmental chemistryEcologyHydrology (agriculture)BiologyChemistryTrophic levelEnvironmental engineeringGeographyGeology

Abstract

fetched live from OpenAlex

We evaluated the role of sewage as a resource for the littoral food web of the fluvial St. Lawrence River near Montreal, Quebec. Stable isotope analysis indicated that macroinvertebrate primary consumers were feeding on local epiphytic production at sites outside the sewage plume, but shifts in δ 15 N of primary and secondary consumers revealed a substantial uptake of sewage-derived resources within the plume, up to 10 km from the outfall. Daily secondary production of macroinvertebrates was 1.8- to 4.1-fold higher at sewage-enriched sites, and the fraction of this production attributable to larval Chironomidae increased from 46% (outside the plume) to 85% (at sewage-enriched sites). Sewage enrichment also stimulated increases in daily fish production based on algivory-detritivory (1.3- to 4.4-fold), invertivory (1.7- to 10-fold), and piscivory (11- to 73-fold). We estimate a daily flux of 13 tonnes of sewage-derived particulate matter, 184 kg of total nitrogen, and 13 kg of total phosphorus into the food web over 1.2 km 2 of the littoral zone within 10 km of the outfall. These values represent no more than a few percent of the total daily discharge of sewage-derived resources but were sufficient to support an overall fivefold increase in secondary production relative to sites outside the plume.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.395
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 teacher head, 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

Citations106
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic SciencesSame topicIsotope Analysis in EcologyFrench-language works237,207