Bibliographic record
Abstract
Permafrost maps have developed over the last century from small line drawings showing the outer limits of the areas within which perennially frozen ground was known or supposed to exist, to large scale, multi-sheet, multi-faceted, complex earth-science documents. These show, in considerable detail, the estimated distribution of frozen ground, in terms of its spatial continuity, thickness, ground temperature and ground ice content. Other related geo-environmental information is commonly included along with the permafrost attributes. The key geocryological issues in permafrost mapping comprise definition, purpose, classification, data acquisition, and data storage and processing. The principal cartographic issues relate to map design, legend development and map production. The recent development of geographic information software (GIS) suitable for use on a desk-top computer allows the geocryologist to undertake many map compilation and production tasks directly. GIS software also allows the map compiler or map user to manipulate the data, layer by layer, and so create specialized maps for specific purposes. Computer storage and processing of permafrost data allows large volumes of data to be handled and, when combined with modelling techniques, allows these large volumes of data to be used in the compilation of maps. Integration of modelling techniques with GIS is a powerful tool for assessing the response of permafrost to a changing climate. Other research directions are noted.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".