Model simulation of the effects of climate variability and change on lake ice in central Alaska, USA
Bibliographic record
Abstract
Abstract The Canadian Lake Ice Model (CLIMo) is used to simulate the effects of climate variability and change on lake ice phenology (freeze-up (FU), break-up (BU), ice-cover duration) and total thickness and composition (snow ice, congelation ice) in central Alaska, USA. The model results suggest that, due to the Pacific Decadal Oscillation shift that occurred in 1976, the ice-cover duration is shorter (4 days) due to earlier BU in spring, and thinner (0.05 m) due to a reduction in the formation of snow ice. Sensitivity tests indicate that air-temperature changes cause the duration change, and snow-depth changes cause the total ice-thickness and composition change. The role of annual and monthly air-temperature and snow-depth changes is examined further in a series of experiments designed to elucidate the possible effects of future climate change. Air temperature is varied in 1˚C increments between –4 and +4˚C, and snow depth is varied in 25% increments between –100% and +100% of the long-term averages for 1952–75 and 1977–2000. The FU and BU dates (ice duration) are most affected by annual air-temperature change. Total ice thickness and composition are most affected by annual and monthly snow-depth change. A key finding is that snow-depth increases cause total ice thickness to increase as a consequence of increased snow-ice formation on top of the ice cover at the expense of congelation-ice formation at the bottom of the ice cover.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".