Biologically induced circulation at fronts
Bibliographic record
Abstract
Consider a frontal region that has high phytoplankton biomass on one side and low biomass on the other. Irradiance penetrates deeply through the water column on the low‐biomass side but is attenuated nearer the surface on the biomass‐rich side because of absorption by phytoplankton. Thus the near‐surface water is heated more on the biomass‐rich side than on the clearer side, resulting in lower‐density surface water on the biomass‐rich side. At greater depths the situation is reversed, with lower‐density water occurring on the biomass‐poor side. We model this situation and examine the resulting perturbations to the frontal circulation. Our aim is to provide an order‐of‐magnitude estimate of the feedbacks from the biological component of the ecosystem to the current field. The model consists of the steady state momentum equations, including Coriolis, pressure gradient, and viscous effects. We compute induced vertical velocities of up to 0.2 mm s−1, commensurate with field measurements and previous modeling estimates of vertical velocities at fronts. The horizontal along‐frontal velocities are of order 2 cm s−1 or less and so will not represent a major contribution to the overall flow field; however, such values are certainly not insignificant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".