Post-Reproductive Pacific Salmon, <em>Oncorhynchus</em> spp., as a Major Nutrient Source for Large Aggregations of Gulls, <em>Larus</em> spp.
Bibliographic record
Abstract
On the Pacific coast of North America, the most abundant vertebrate visitors to estuaries and rivers during salmon migration are gulls, yet the utilization of salmon nutrients by these scavengers, and subsequent ecological impacts are not well documented. On two forested watersheds on the central coast of British Columbia, we tracked gull abundance during the spawning period for two consecutive years, and estimated consumption of post-reproductive salmon carcasses and eggs, as well as guano production. At Clatse River, gulls (Larus glaucescens, L. argentatus, L. thayerii, L. californicus, L. canus, L. philadelphia) consumed 13-26% of total salmon carcass biomass and 29-36% of all salmon eggs deposited in the system. At Neekas River, gulls consumed 11-19% of salmon carcass biomass and 7-18% of total salmon eggs. Local guano production over the 60-day period ranged from 600 kg to 1190 kg at Clatse and from 1200 kg to 2100 kg at Neekas River, and was distributed to marine, estuarine, freshwater and riparian habitats. The large aggregations of gulls and subsequent nutrient cycling observed on our study watersheds may represent a once widespread phenomenon that is now largely reduced due to recent declines in salmon populations.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".