Data from: Defining isoscapes in the Northeast Pacific as an index of ocean productivity
Bibliographic record
Abstract
Aim: We modeled isoscapes in the Northeast Pacific using satellite-based data with the main objective of testing if isoscapes defined by a few key parameters can be used as a proxy for secondary productivity. Location: Northeast (NE) Pacific; 46 – 60⁰N and 125 – 165⁰W. Time period: From 1998 to 2017 (ongoing). Major taxa studied: Zooplankton with a focus on large herbivores. Methods: Approximately 280 summer zooplankton samples were analyzed for Carbon (δ13C) and Nitrogen (δ15N) stable isotope (SI) ratios. Environmental conditions experienced by zooplankton organisms were extracted from satellite, in situ sensor and model databases. A generalized additive model approach was used to explain the spatial variability of δ13C and δ15N values and predict isoscapes. Results: Sea surface temperature (SST), sea level anomaly (SLA) and chlorophyll-a concentration emerged as the significant SI predictors. Modelled isoscapes reproduced patterns observed in δ13C and δ15N value distribution, such as a decrease from the coast to offshore. The contribution of eddies in enhancing local production in the open ocean was also well captured by the models. In the central part of the NE Pacific higher SI values were correlated with higher large copepod biomass measured by the North Pacific Continuous Plankton Recorder (CPR) survey. However, in the area off the coast of British Columbia (BC) high δ15N variability appeared to be associated with episodic intrusions of coastal waters demonstrating that caution is needed when interpreting sharp changes in SI ratios. Main conclusions: While the mechanisms driving SI ratio variability are complex, we demonstrated that a few parameters used as a proxy for some of these major mechanisms are able to successfully produce isoscape models. This approach was proven useful to provide a qualitative estimate of the secondary production, which can be particularly valuable in a region where few data are available.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".