Pacific herring spawns transfer energy to coastal ecosystems
Bibliographic record
Abstract
Through their interspecific interactions, Pacific herring (Clupea pallasii) are foundational to coastal marine ecosystems in the North Pacific Ocean. During annual herring spawns, hundreds of thousands of individuals migrate to sheltered nearshore waters, where males release sperm and females deposit millions of adhesive eggs onto substrates such as seagrass, kelp, and rock. This aggregation of herring biomass results in a pulse of energy and nutrients that is transferred to coastal ecosystems via predation by species throughout the food web, including cetaceans, pinnipeds, fish, invertebrates, birds, and terrestrial mammals such as bears and wolves. This photograph shows Pacific herring eggs deposited on seaweed during the March 2022 spawn in the territory of the Lekwungen peoples at the Fisgard Lighthouse National Historic Site (British Columbia, Canada). We observed many species feeding on Pacific herring and their progeny, including sea lions, seals, river otters, bald eagles, seabirds, and shorebirds. Pacific herring populations in British Columbia are declining, in part due to commercial (over)fishing. The resulting loss of energy and nutrients from fewer herring spawns could alter the species interactions and structure of coastal marine, intertidal, and supratidal communities. However, sustainable harvest by coastal First Nations continues and includes harvesting herring roe on kelp and cedar boughs without catching the fish themselves. By not harvesting the fish themselves, this allows for the maintenance of older, more experienced, and more fecund individuals, which may help to recover Pacific herring populations and food-web interactions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".