Field observations and results of a 1-D boundary layer model for developing near-surface temperature maxima in the Western Arctic
Bibliographic record
Abstract
Summer sea ice extent in the Western Arctic has decreased significantly in recent years resulting in increased solar input into the upper ocean. Here, a comprehensive set of in situshipboard, on-ice, and autonomous ice-ocean measurements were made of the early stages of formation of the near-surface temperature maximum (NSTM) in the Canada Basin. These observations along with the results from a 1-D turbulent boundary layer model indicate that heat storage associated with NSTM formation is largely due to the absorption of penetrating solar radiation just below a protective summer halocline. The depth of the summer halocline was found to be the most important factor for determining the amount of solar radiation absorbed in the NSTM layer, while halocline strength controlled the amount of heat removed from the NSTM by turbulent transport. Observations using the Naval Postgraduate School Turbulence Frame show that the NSTM was able to persist despite periods of intermittent turbulence because transport rates were too small to remove significant amounts of heat from the NSTM layer. The development of the early and late summer halocline and NSTM were found to be linked to summer season buoyancy and wind events. For the early summer NSTM, 1-D boundary layer model results show that melt pond drainage provides sufficient buoyancy to the summer halocline to prevent subsequent wind events from mixing out the NSTM. For the late summer NSTM,limited freshwater inputs reduce the strength of the summer halocline making the balance between interfacial stresses and buoyancy more tenuous. As a result, the late summer NSTM is an ephemeral feature dependent on local wind conditions, while the early summer NSTM is more persistent and able to store heat in the near-surface ocean beyond the summer season.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".