A functional regression model for predicting optical depth and estimating attenuation coefficients in sea-ice covers near Resolute Passage, Canada
Bibliographic record
Abstract
Abstract The spectral dependence of natural light transmittance on ice algae concentration and snow depth in Arctic sea ice provides the potential to study the changing bottom-ice ecosystem using optical relationships. In this paper, we consider the use of functional data analysis techniques to describe such relationships. Specifically, we created a functional regression model describing spectral optical depth as a function of chlorophyll a concentration, snow depth and ice thickness. Measurements of the aforementioned covariates and surface and transmitted spectral irradiance were collected on landfast first-year sea ice in the High Arctic near Resolute Passage, Canada, during the spring of 2011 and used as model input. The derived model explains 75–84.5% of the variation in the observed spectral optical depth curves. No prior assumptions of snow/sea-ice optical properties are required in the application of this technique, as the model estimates the attenuation coefficients of each covariate using only the measurements mentioned above. The quality and simplicity of the model highlight the potential of functional data analysis to study the Arctic marine ecosystem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".