Bayesian Estimates of Ice Optical Properties for Lake Ice Modeling
Bibliographic record
Abstract
Abstract Ice and snow cover on frozen lakes is a natural barrier to solar radiation, reducing the transfer of energy that controls under‐ice thermal dynamics and biological productivity. Direct measurements of under‐ice irradiance remain scarce due to logistical constraints. We assembled data from 46 freshwater lakes across the Northern Hemisphere, including 722 daily irradiance observations and ice and snow thickness records. Ice quality data (black ice, white ice, and snow cover) were available for 15 lakes (626 measurements). Using this data set and a Bayesian implementation of the Beer‐Lambert law, we estimated statistical distributions of albedo and attenuation coefficients. Median albedo values were 0.55 for black ice, 0.60 for white ice, 0.56 for total ice, and 0.94 for snow, with corresponding attenuation coefficients of 0.79, 4.35, 1.75, and 8.96 , respectively. These refined optical properties address critical data gaps, improving under‐ice irradiance predictions and enhancing understanding of lake processes under climate‐driven ice conditions.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".