Clustering coupled biochemical-physical model results formalizes regional provinces in a coastal region
Bibliographic record
Abstract
Coastal regions by their very nature are dynamically diverse. Within one geographical region there are often multiple areas dominated by substantially different dynamics that shape not only the physical characteristics but also the ecosystem. The Salish Sea, in the northeast Pacific, is an excellent example with strongly tidally mixed regions, freshwater-dominated regions, and regions directly influenced by the open ocean. These regions are generally well known and multiple disciplines refer to them with various boundaries and under various names. Here we use unsupervised clustering on numerical model results to formalize these regional provinces. The model is SalishSeaCast, a three-dimensional real-time coupled bio-chem-physical model based on the NEMO framework. We find that the regions clustered on ecosystem variables (phytoplankton biomass) spatially coincide with those clustered on physical variables, particularly the stratification as diagnosed by the halocline depth. The clusters are robust across years with interannual variability manifesting mostly in changes in the size of the clusters. As the clusters are dynamically distinct, they provide a natural framework on which to evaluate the model against observations. We find that the model accurately simulates each of the major clusters. The spatial and temporal resolution of the model can then characterize these different clusters more systematically than the observations, revealing biases associated with sparse sampling in the observations. Two examples will be given, one addressing a long-standing issue of the productivity gradient in the stratified main basin, the Strait of Georgia, and another concerning the seasonal cycle of productivity in the ocean-influenced Juan de Fuca Strait.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".