Ocean surface characteristics influence recruitment variability of populations of northern shrimp (Pandalus borealis) in the Northwest Atlantic
Bibliographic record
Abstract
Abstract Ouellet, P., Fuentes-Yaco, C., Savard, L., Platt, T., Sathyendranath, S., Koeller, P., Orr, D., and Siegstad, H. 2011. Ocean surface characteristics influence recruitment variability of populations of northern shrimp (Pandalus borealis) in the Northwest Atlantic. – ICES Journal of Marine Science, 68: 737–744. Remotely sensed data were used to derive simple ecosystem indicators for four regions of the Northwest Atlantic to test the hypothesis that sea surface temperatures (SSTs) and spring phytoplankton bloom characteristics (initiation, timing, intensity, and duration) have a significant influence on larval survival and recruitment of northern shrimp (Pandalus borealis). For all years (1998–2007) and regions, hatching was after the initiation of the bloom and before or after the bloom reached its maximum intensity. The results suggest that the best survival of larvae is associated with high warming rates of SST following hatching, but in very cold environments, warm temperatures at hatching seem to be important for larval survival. The analyses also indicate that larval survival is supported by an early, long phytoplankton bloom which attains high concentrations of chlorophyll a. The results demonstrate the potential of remotely sensed data for deriving simple population-specific ecosystem indicators for potential use in building operational recruitment models for predicting changes in northern shrimp abundance.
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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 | 0.001 |
| 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".