Drivers of Physical and Biological Frontal Variability in the Northern California Current System
Bibliographic record
Abstract
Abstract Oceanic fronts mark the boundary between two water masses and are often sites of complex bio‐physical processes and multi‐trophic level interactions, making them particularly important features in marine ecosystems. As global climate change induces multi‐scale shifts in the driving physical mechanisms of fronts, spatiotemporal tracking of frontal variability can aid in efforts to understand the downstream effects on marine biodiversity and ecosystem structure. Here we focus on fronts within the dynamic northern extent of the California Current System (NCC). We derived mesoscale sea surface temperature (SST) and chlorophyll‐ a (chl‐ a ) fronts across the NCC region from 4‐km MODIS‐Aqua L3 daily fields over 2003–2019. Mesoscale physical (SST) and biological (chl‐ a ) fronts were often adjacent and coherent in their seasonal and interannual occurrence frequencies, but were spatially decoupled. SST fronts were most frequent and broadly distributed offshore while chl‐ a fronts mostly occurred along the continental shelf break, particularly from Vancouver Island to central Oregon. Additionally, we employed a standardized multiple linear regression analysis to quantify the relative influence of local‐ and basin‐scale processes on frontal variability in the NCC. Local wind stress and wind stress curl variability were the most influential drivers of fronts over the shelf, while basin‐scale climate variability (i.e., climate oscillations) significantly drove frontal occurrences along the shelf break and offshore. Given predictions in the intensification of coastal upwelling in systems such as the NCC, our results indicate that oceanic response to climate change driven atmospheric variability will significantly impact the NCC marine ecosystem on the mesoscale.
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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 | 0.000 | 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) | 0.001 | 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".