Bibliographic record
Abstract
Abstract The critical depth hypothesis (CDH) is a predictive criteria for the onset of phytoplankton blooms that comes from the steady-state analytical solution of a simple mathematical model for phytoplankton growth presented by Sverdrup in 1953. Sverdrup's phytoplankton-only model is very elementary compared with state-of-the-art ecosystem models whose numerical solution in a time-varying environment do not systematically conform to the CDH. To highlight which model ingredients make the bloom onset deviate from the CDH, the complexity of Sverdrup's model is incrementally increased, and the impact that each new level of complexity introduced is analysed. Complexity is added both to the ecosystem model and to the parameterization of physical forcing. In the most complete experiment, the model is a one-dimensional Nutrient-Phytoplankton-Zooplankton model that includes seasonally varying mixed layer depth and surface irradiance, light and nutrient limitation, variable grazing, self-shading, export, and remineralization. When complexity is added to the ecosystem model, it is found that the model solution only marginally deviates from the CDH. But when the physical forcing is also changed, the model solution can conform to two competing theories for the onset of phytoplankton blooms—the critical turbulence hypothesis and the disturbance recovery hypothesis. The key roles of three physical ingredients on the bloom onset are highlighted: the intensity of vertical mixing at the end of winter, the seasonal evolution of the mixed-layer depth from the previous summer, and the seasonal evolution of surface irradiance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".