A Diagnostic Model for Mixed Layer Depth Estimation with Application to Ocean Station P in the Northeast Pacific
Bibliographic record
Abstract
Abstract This paper presents a simple diagnostic model for estimating mixed layer depth based solely on the one-dimensional heat balance equation, the surface heat flux, and the sea surface temperature. The surface fluxes drive heating or cooling of the upper layer whereas the surface temperature acts as a “thermostat” that regulates the vertical extent of the layer. Daily mixed layer depth estimates from the diagnostic model (and two standard bulk mixed layer models) are compared with depths obtained from oceanic profiles collected during the 1956–80 Canadian Weathership program at Station P and more recent (2001–07) profiles from the vicinity of this station from Argo drifters. Summer mixed layer depths from the diagnostic model agree more closely with observed depths and are less sensitive to heat flux errors than those from bulk models. For the Weathership monitoring period, the root-mean-square difference between modeled and observed monthly mean mixed layer depths is ∼6 m for the diagnostic model and ∼10 m for the bulk models. The diagnostic model is simpler to apply than bulk models and sidesteps the need for wind data and turbulence parameterization required by these models. Mixed layer depths obtained from the diagnostic model using NCEP–NCAR reanalysis data reveal that—contrary to reports for late winter—there has been no significant trend in the summer mixed layer depth in the central northeast Pacific over the past 52 yr.
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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 | 0.001 |
| 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".