Statistical validation of a 3-D bio-physical model of the western North Atlantic
Bibliographic record
Abstract
Abstract. High-resolution, physical-biological models of coastal and shelf regions typically use a single functional phytoplankton group, which limits their ability to represent ecological gradients (e.g. highly productive shelf systems adjacent to oligotrophic regions), as these are dominated by different functional phytoplankton groups. We implemented a size-structured ecosystem model in a high-resolution, regional circulation model of the northeast North American shelf and adjacent deep ocean in order to assess whether the added functional complexity of two functional phytoplankton groups improves the model's ability to represent surface chlorophyll concentrations along an ecological gradient encompassing five distinct regions. We used satellite-derived SST and sea-surface chlorophyll for our model assessment, as these allow investigation of spatial variability and temporal variations from monthly to interannual, and analyzed three complimentary statistical measures of model-data agreement: model bias, root mean square error and model efficiency (or skill). All three measures were integrated for the whole domain, for distinct subregions and were calculated in a spatially explicit manner. Comparison with a previously published simulation that used a model with a single phytoplankton functional group indicates that the inclusion of an additional phytoplankton group representing picoplankton markedly improves the model's skill.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".