Lake Ice From Historical Records to Contemporary Science
Bibliographic record
Abstract
Abstract Lake ice phenology is a critical component of the cryosphere and a sensitive indicator of climate change that has some of the longest records related to climate science. Records commenced for numerous reasons including navigation, hydropower development, and individual curiosity, demonstrating the value of lake ice as a seasonal event of significant importance to a broad swath of peoples and countries. At the same time, lake ice loss has been rapid and widespread with lakes losing ice at an average rate of 17 days per century. In this Perspective, we examine the earliest known records of ice cover and the scientific studies that developed from that practice of record keeping. Studies in lake ice began in the nineteenth Century and have included relationships between climate, biology, and ice cover. Early studies developed some of the foundational principles that limnologists and climate scientists are still exploring, such as the relationship between ice phenology and climate variables, large‐scale climate oscillations, and morphological characteristics, with implications for lake ice physical structure and under‐ice ecosystems in a warming climate. We conclude with an examination of the state of the field and how these centuries‐long lake ice records can continue to inform cutting edge science by validating satellite remote sensing techniques, in addition to modeling approaches and collaborations across disciplines, that can improve our understanding of the loss of lake ice in a warmer world.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".