The harmful effects of permafrost melt: the release of greenhouse gases and damage to infrastructure
Bibliographic record
Abstract
A Word from the Editor It feels disturbingly fitting that I write this note as we experience a year in Ottawa where I have felt the effects of climate change most tangibly. Amidst a fairly mild winter, followed by many tornado warnings, torrential rainstorms, and intense heat waves, it is evident that our climate is becoming increasingly volatile. In this article, Szaranski (2023) illustrates how climate change negatively impacts global communities via its effect on permafrost, an impact that is disproportionately felt by Arctic populations. Further, the article details how the environmental impacts of climate change on permafrost create a cyclical effect that ultimately accelerates global warming. This article will provide you with the necessary information to think critically about how to best address environmental degradation and what it means to be a global citizen.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".