Bibliographic record
Abstract
ASTIS NewsThe Arctic Science and Technology Information System (ASTIS), Canada's northern publications and research projects database, now contains over 76 600 records and provides links to PDF files of more than 20 200 publications.This database can be accessed for free from the AINA website at www.aina.ucalgary.ca/astis.ASTIS also supports 15 subset databases, three of which experienced noteworthy growth in the past few months.The Nunavik Bibliography now describes over 7000 records on Quebec north of 55˚ and some adjacent regions.It covers all aspects of Nunavik including the earth sciences, life sciences, engineering and technology, renewable and nonrenewable resources, co-management, land use, people, government, economic and social conditions, archaeology, history, art, and literature.This database is a joint project of the Makivik Corporation, Aboriginal Affairs and Northern Development Canada, the Canadian Circumpolar Institute, the Arctic Institute of North America, and the Centre d'études nordiques.This is a cooperative long-term project to build a comprehensive bibliographic database about northern Quebec, but at present it covers only part of the available literature.The Nunavik Bibliography is available at www.aina.ucalgary.ca/nunavik.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.349 | 0.240 |
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".