Bio‐optical and structural properties inferred from irradiance measurements within the bottommost layers in an Arctic landfast sea ice cover
Bibliographic record
Abstract
Irradiance spectra were measured at vertical increments within the bottommost layers of landfast sea ice with the aid of divers in Franklin Bay, Canada, in an effort to obtain input parameters for bio‐optical modeling of sea ice. The study took place between 22 April and 9 May 2004 during the overwintering stage of CASES (Canadian Arctic Shelf Exchange Study). The ice was about 1.8 m thick with a snow cover of variable thickness (∼0.04 to 0.4 m). Ice surface temperatures increased from about −12° to −6.4°C during the sampling period, while ice temperatures within the bottommost portion under study ranged from −3.0° to −1.2°C. Ice algae were visible within the bottommost centimeters of the sea ice. This algae layer had a marked effect on the spectral distribution of transmitted irradiance beneath the ice. Particulate absorption spectra, ap(λ), measured from melted ice samples showed evidence of chloroplastic pigment degradation and could not fully explain the shape of the in situ diffuse attenuation coefficient, Kd(λ), for the algal layer. Interior ice layers, however, did show absorption curves similar to ap(λ) from samples, suggesting the presence of degraded algal pigments within these layers. The discrete ordinates radiative transfer (DISORT) code was iterated in an inverse approach to estimate ap(λ) and the scattering coefficient, btot, from the irradiance profiles. For the bottom 0.1 m of the sea ice, btot was around 400 m−1, while at the 0.1‐ to 0.2‐m layer from the ice bottom it decreased to 165 m−1. Using ap(λ) combined with wavelength independent btot as inputs to DISORT seem to adequately explain the radiative transfer near the bottom of first‐year sea ice provided that adjustments were made to the brine volume fraction.
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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".