The Blob and Queen Charlotte: Predicting Ocean Properties in an Upwelling System during Anomalous Conditions
Bibliographic record
Abstract
From 2014 to at least 2018, ecosystem health in the eastern boundary upwelling system along the west coast of North America was significantly impacted by a combination of a marine heatwave known as The Blob and an El Niño event, as well as by ongoing climate change. At the northern limit of this upwelling system, in Queen Charlotte Sound on the highly productive central coast of British Columbia, we have demonstrated that changing conditions on the continental shelf and in coastal waters may be skillfully predicted based on observed open-ocean and large-scale atmospheric conditions on seasonal to interannual timescales. In this work, we build on our understanding of this predictability by presenting a statistical model that relates physical and biogeochemical ocean properties in this region to conditions at and beyond the shelf break and large-scale forcing metrics. The model is based on statistical relationships developed using a multi-decadal archive of hydrographic and biogeochemical data in combination with high-temporal-resolution mooring records collected in Queen Charlotte Sound, and is supported by a conceptual understanding of the upwelling and downwelling regimes in this region. We next use the model to examine specifically how the arrival of The Blob and the subsequent El Niño modified ocean conditions on the continental shelf during both upwelling and downwelling, including impacts on nutrient concentrations, dissolved oxygen levels, stratification, and warming. Our results suggest it may be possible to predict changes in this upwelling system caused by future anomalous events and climate change using readily available large-scale data products such as the Argo dataset and NOAA Upwelling Index.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".