An automated method to monitor lake ice phenology
Bibliographic record
Abstract
A simple method to automatically measure the date of ice‐on, the date of ice‐off, and the duration of lake ice cover is described. The presence of ice cover is detected by recording water temperature just below the ice/water interface and just above the lake bottom using moored temperature sensors. The occurrence of ice‐on rapidly leads to detectible levels of inverse stratification, defined as existing when the upper sensor records a temperature at least 0.1°C below that of the bottom sensor, whereas the occurrence of ice‐off leads to the return of isothermal mixing. Based on data from 10 lakes over a total of 43 winter seasons, we found that the timing and duration of inverse stratification monitored by recording temperature sensors compares well with ice cover statistics based on human observation. The root mean square difference between the observer‐based and temperature‐based estimates was 7.1 d for ice‐on, 6.4 d for ice‐off, and 10.0 d for the duration of ice cover. The coefficient of determination between the two types of estimates was 0.93, 0.86, and 0.91, respectively. The availability of inexpensive self‐contained temperature loggers should allow expanded monitoring of ice cover in a large and diverse array of lakes. Such monitoring is needed to improve our ability to monitor the progression of global climate change, and to improve our understanding of the relationship between climate and ice cover over a wide range of temporal and spatial scales.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".