Influence of snow cover and algae on the spectral dependence of transmitted irradiance through Arctic landfast first‐year sea ice
Bibliographic record
Abstract
Extensive spatial and temporal observations of sea ice algae remain limited due in part to current destructive and time intensive sampling techniques. In this paper we examine the influence of snow cover and ice algal biomass on the spectral dependence of photosynthetically available radiation transmitted through the snow‐ice matrix using a data set collected in Resolute Passage, Canada, from 3 to 21 May 2003. The relationships between a normalized difference index (NDI) of transmitted irradiance with ice algal biomass and with snow cover provided a means to examine and compare observational and modeled data. In contrast to the dominant scattering properties of snow, absorption largely controls the spectral diffuse attenuation coefficient of algae. Our results show that snow has little effect on the distribution of transmitted spectral irradiance at wavelengths between 400 and 550 nm, whereas algae have a strong absorption peak near 440 nm that dominates changes in spectral transmission across this wavelength range. Up to 89% of the total variation in algae biomass was accounted for with a single NDI wavelength combination. Therefore the blue wavelength peak in algal spectral absorption lends particularly well to the remote estimation of algae biomass using transmitted irradiance. Deviations between observed and modeled data highlight the need for improvements to model inputs and therefore more detailed observations of processes controlling snow, ice, and algae in situ optical properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".