Simulated current and projected radiation balance of a High Arctic lake during the open water season
Bibliographic record
Abstract
The Arctic is warming four times faster than the global average, with lake ice cover duration shortening, increasing the role of lakes within the regional radiation balance. Studying Arctic lakes is logistically difficult; through ice cover modelling with the Canadian Lake Ice Model we project simulated radiation balance changes for a typical small High Arctic Lake to 2100 using the 0.44° horizontal grid from CoOrdinated Regional climate Downscaling Experiment, Representative Concentration Pathway 8.5 scenario. The open water season is projected to extend 90–100 days longer than the current open water season. During the open water period, net longwave radiation is projected to increase by ∼0.4 Wm−2 per decade. Annually, net and shortwave radiation are projected to increase (∼1 and 1.3 Wm−2 per decade), and longwave radiation is projected to decrease (0.4 Wm−2 per decade). The projected trends show that the largest net shortwave radiation increases are expected to occur in May and June, whereas net longwave radiation is projected to have the largest decrease in July. The role of lakes in the regional energy balance is increasing because of the lengthening open water season through increases in lake evaporative losses and warmer water temperatures, which favour invasive species and will negatively impact cold-water fish and organisms that are well adapted to ice-covered 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".