Northwest Territories Geological Survey Permafrost Science Team Strategic Plan (2021-2024)
Bibliographic record
Abstract
Over the past several years, despite limited capacity, the Northwest Territories Geological Survey (NTGS) has increasingly been looked to as a source of permafrost knowledge by the Government of Northwest Territories (GNWT) and others in the broader permafrost community. The NTGS has recently added three new positions to create a Permafrost Science Team. At present, the NTGS Permafrost Science Team consists of four positions: a Senior Permafrost Scientist, a Permafrost Scientist, a Permafrost Data Scientist, and a Permafrost Geohazard Scientist. The NTGS Permafrost Science Team has developed its first strategic plan to anticipate increased interest in and need for permafrost science and expertise. This plan is intended to clarify the purpose and focus of the NTGS Permafrost Science Team, and to provide a planning framework for GNWT permafrost science collaborators and external collaborators and partners. This strategic plan will help the staff work with collaborators and partners and engage with clients more effectively, and prioritise their work on an ongoing basis. It will also help the leadership of NTGS and partnering organisations support the success of this growing team. The plan is meant to be interpreted within the broader NTGS planning framework and is expected to evolve.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.028 |
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".