The euphotic zone under Arctic Ocean sea ice: Vertical extents and seasonal trends
Bibliographic record
Abstract
Abstract Eight Ice‐Tethered Profilers were deployed in the Arctic Ocean between 2011 and 2013 to measure vertical distributions of photosynthetically active radiation (PAR) and other bio‐optical properties in ice‐covered water columns, multiple times a day over periods of up to a year. With the radiometers used on these profilers, PAR could be measured to depths of only ∼20–40 m in the central Arctic in late summer under sea ice ∼1 m thick. At lower latitudes in the Beaufort Gyre, late summer PAR was measurable under ice to depths exceeding 125 m. The maximum depths of measurable PAR followed seasonal trends in insolation, with isolumes shoaling in the fall as solar elevation decreased and deepening in spring and early summer after insolation resumed and sea ice diminished. PAR intensities were often anomalously low above 20 m, likely due to a shading effect associated with local horizontal heterogeneity in light transmittance by the overlying sea ice. A model was developed to parameterize these complex vertical PAR distributions to improve estimates of the water column diffuse attenuation coefficient and other related parameters. Such a model is necessary to separate the effect of surface ice heterogeneity on under‐ice PAR profiles from that of the water column itself, so that euphotic zone depth in ice‐covered water columns can be computed using canonical metrics such as the 1% light level. Water column diffuse attenuation coefficients derived from such autonomously‐collected PAR profile data, using this model, agreed favorably with values determined manually in complementary studies.
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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 | 0.001 |
| 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 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".