Impact of fertilization and stocking on trophic interactions and growth of juvenile sockeye salmon (<i>Oncorhynchus nerka</i>)
Bibliographic record
Abstract
Using 16 years of data on nutrients, plankton, and sockeye fry and smolts from Packers Lake, Alaska, we test the impact of nutrients and fry stocking on the growth and productivity of juvenile sockeye salmon (Oncorhynchus nerka). To enhance sockeye production, this lake was fertilized (19831996) and stocked annually (19871996) with sockeye fry. Before fertilization, the density of sockeye fry was low (<0.20 fry·m2), the size and biomass of Daphnia were low, and sockeye smolts were relatively small. Before stocking, all trophic levels responded positively to fertilization. The biomass and mean size of Daphnia increased significantly. The average size of age-1 and age-2 smolts increased three- to four-fold. Fry stocking produced dramatic declines in both biomass and mean length of Daphnia and in size of smolts. When large-sized (>1 mm) Daphnia were significantly reduced in density under heavy predation by sockeye fry, the growth of juvenile sockeye declined, even under continued fertilization. We show that fry density and associated food web structure are major determinants of juvenile sockeye responses to fertilization and stocking. This study probably provides the first long-term experimental results linking limnological and nutrient food web concepts to trophodynamics and productivity of juvenile sockeye salmon.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".