Assessment of light absorption within highly scattering bottom sea ice from under‐ice light measurements: Implications for Arctic ice algae primary production
Bibliographic record
Abstract
Primary production estimates of ice algae within the bottommost layers of the Arctic ice cover are commonly derived using irradiance measurements taken immediately below the solid ice bottom. However, radiation absorbed by ice algae is significantly affected by the high‐scattering sea ice environment they are embedded within because scattering increases the pathlength traveled by photons and therefore the probability of photon encounters with algal cells. Failing to account for this enhanced absorption may considerably affect estimates of the timing and magnitude of ice algal production. To demonstrate the effect of scattering and attenuation, multipliers for absorption amplification (Φ) and layer average opacity (χ) were derived from observations of chlorophyll a concentration and the vertical attenuation coefficient over the bottom 2.5 cm of landfast sea ice. Φ reached values over 19 at low chlorophyll a, but became < 2 at high biomass levels, whereas χ became larger as biomass levels increased. Using Φ to construct an apparent photosynthesis vs. irradiance relationship showed that light limitation is greatly reduced relative to the case where scattering is not considered. This highlights an important interaction not previously noted for ice algal production in their high‐scattering environment. Knowledge of this absorption amplification can help explain ice algal phenology during the spring bloom and will improve ice algal production estimates and model parameterizations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".