Stratification, plankton layers, and mixing measured by airborne lidar in the Chukchi and Beaufort seas
Bibliographic record
Abstract
A total of 4.9 million vertical profiles of optical backscattering were measured by airborne lidar in July of 2014 and July of 2017 in the Chukchi and Beaufort seas. We found very different ice conditions in the study area between July 2014 and July 2017, but the characteristics of subsurface plankton layers measured by the lidar and their dependence on ice cover were similar for the two years. In both years, the prevalence of subsurface plankton layers exponentially decreased with increasing ice cover. The average depths were similar for both years, with layers in open water deeper than those in the pack ice. The depths of subsurface plankton layers were consistent with mixed layer depth in areas where in situ density profiles were available. A noticeable difference in layer strength (defined as the ratio of the layer signal to the background) was likely caused by higher background phytoplankton concentrations in 2017. Differences in layer thickness were observed, which could be the result of higher current shears in 2017. Turbulent mixing of phytoplankton and zooplankton in Barrow Canyon was inferred from the power spectral density of lidar and acoustic scattering. Lidar measurements suggested that the level of turbulence and its vertical distribution were affected by local upwelling-favorable winds. The vertical distribution of acoustic scattering was different from that of the lidar, which we interpret as different vertical distributions of phytoplankton and zooplankton.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".