Bibliographic record
Abstract
The geochemistry resulting from permafrost freeze-thaw processes are poorly understood. This EGP activity aims to refine our current understanding of these processes, and how they are impacted by infrastructure development. This study will investigate the impacts on permafrost geochemistry associated with the construction of the Inuvik to Tuktoyaktuk Highway (ITH). It will investigate both the ITH development itself, and a gravel pit used as source material for ITH construction. However, COVID19-related restrictions meant that no fieldwork was possible in 2020-21. This situation means that there is no data to report for this fiscal year in terms of results and interpretations. This will continue until access to field sites is again permitted. Nonetheless, there were significant achievements for this project in the 2020-21 fiscal year, which include: 1) purchasing, through combined EGP - GEM-GeoNorth funding, arrays of sensors that will facilitate understanding of the geochemistry of permafrost active layer freeze-thaw processes; 2) development of a companion project within GEM-GeoNorth that aims to look at a wider array of permafrost processes; 3) continued engagement with Inuvialuit stakeholders to keep them up-to-date with respect to this project; 4) SLN capital purchase, in response to this projects request, of a cryogenic stage for laser ablation of ice - which will greatly advance the geochemical investigative arsenal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".