Drivers of variation in crustacean zooplankton production rates differ across regions off the west coast of Vancouver Island and in the subarctic NE Pacific
Bibliographic record
Abstract
Abstract The subarctic NE Pacific is comprised of several oceanographic regimes, in which regional variability in sea surface temperature (SST), satellite chlorophyll a, and crustacean zooplankton biomass influence the production rates of crustacean zooplankton. Traditional methods for estimating zooplankton production rates are labour/time intensive and restricted to select copepod species. A practical field alternative is the ‘chitobiase method’, which yields community-level biomass production rates (BPR) analogous to traditional moulting rate methods. BPR was measured along the west coast of Vancouver Island and in the subarctic NE Pacific during 2005, 2009–2011, and 2015–2018. Generalized additive modelling identified SST and the developing crustacean zooplankton biomass as key drivers of BPR variability. BPR varied positively with the proportion of developing copepod biomass relative to that of non-copepod crustacean zooplankton biomass. Our analysis indicates that BPR variation is associated with zooplankton community composition, but that high zooplankton biomass is not necessarily predictive of high BPR. Specifically, higher BPR is associated with a higher relative biomass of large-bodied, cold-water indicator species, and a lower biomass of non-copepod crustaceans.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".