Apparent optical properties of the Canadian Beaufort Sea – Part 1: Observational overview and water column relationships
Bibliographic record
Abstract
Abstract. A data set of radiometric measurements collected in the Beaufort Sea (Canadian Arctic) in August 2009 (Malina project) is analyzed in order to describe apparent optical properties (AOPs) in this sea, which has been subject to dramatic environmental changes for several decades. The two properties derived from the measurements are the spectral diffuse attenuation coefficient for downward irradiance, Kd, and the spectral remote sensing reflectance, Rrs. The former controls light propagation in the upper water column. The latter determines how light is backscattered out of the water and becomes eventually observable from a satellite ocean color sensor. The data set includes offshore clear waters of the Beaufort Basin as well as highly turbid waters of the Mackenzie River plumes. In the clear waters, we show Kd values that are much larger in the ultraviolet and blue parts of the spectrum than what could be anticipated considering the chlorophyll concentration. A larger contribution of absorption by colored dissolved organic matter (CDOM) is responsible for these high Kd values, as compared to other oligotrophic areas. In turbid waters, attenuation reaches extremely high values, driven by high loads of particulate materials and also by a large CDOM content. In these two extreme types of waters, current satellite chlorophyll algorithms fail. This questions the role of ocean color remote sensing in the Arctic when Rrs from only the blue and green bands are used. Therefore, other parts of the spectrum (e.g., the red) should be explored if one aims at quantifying interannual changes in chlorophyll in the Arctic from space. The very peculiar AOPs in the Beaufort Sea also advocate for developing specific light propagation models when attempting to predict light availability for photosynthesis at depth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".