The role of sewage in a large river food web
Bibliographic record
Abstract
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 δ15N 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 km2 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 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 | 0.001 |
| 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".