Bibliographic record
Abstract
The permafrost region occupies approximately half of Canada's land mass. Knowledge of the distribution of permafrost and its physical properties is critical for understanding terrain stability in Arctic environments and is required information for any sort of infrastructure development (community, transportation or natural resource sector). This knowledge is becoming increasingly important as climate changes, since the distribution and characteristics of permafrost are highly correlated with climatic conditions. The Earth Science Sector (ESS) provides Canada with multi-disciplinary permafrost expertise. Researchers at Canada Centre for Remote Sensing (CCRS) apply information and data from a range of satellites to assist in mapping and better understanding permafrost environments as well as conducting change-detection studies. Researchers at the Geological Survey of Canada (GSC) employ a variety of field-based and thermal monitoring techniques to characterize permafrost and better understand terrain processes that may result under a changing climate. Both groups utilize various numerical modelling techniques to explain observed phenomena and to predict future conditions. This workshop on Permafrost Science was convened to highlight the various types of permafrost expertise in ESS. In addition, this workshop identified gaps and possible opportunities for new collaborative permafrost research.
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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 | 0.023 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".