Spatially Distributed Modelling of Lake Ice Trends and Distribution in the North Slave Region, NWT, Canada
Bibliographic record
Abstract
The Canadian Lake Ice Model has been distributed and adapted to run at a fine spatial resolution (~50 m) for simulating lake ice thickness and phenology on small to medium-sized lakes for this study. The model’s capabilities are extended to simulate the spatial variability of Lake ice thickness (LIT), Ice Cover Duration (ICD), and Lake Ice Phenology (LIP) across 500 predominantly small to medium-sized lakes in the North Slave Region (NSR) of the Northwest Territories (NWT), Canada, from 1984 to 2022. The model utilizes 30 m grid lake surface temperature (LST) data derived from the North Slave LST dataset, along with climate inputs from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 and ECMWF (ERA5), including wind speed, mean air temperature, relative humidity, snow depth, and cloud cover. These inputs provide surface fluxes to the model, driving its unsteady heat equation to produce daily LIT, annual ICD, and annual freeze-up and break-up dates on a ~50 m grid. Validation against in-situ measurements showed a root mean square deviation of 2.7 cm to 7 cm for LIT. Trend analysis revealed a significant decline in LIT (-0.26 cm/year to -0.10 cm/year) and ICD (-0.40 day/year to -0.15 day/year) over the study period. The findings highlight the sensitivity of LIT and freeze-up dates to lake morphometry. In contrast, ice break-up dates are primarily influenced by geographic factors such as latitude and elevation. The distributed model comprehensively assesses lake ice variability and trends across 500 lakes under changing climate and weather conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".