Data Management Activities of Canada's National Science Library - 2010 Update and Prospective
Bibliographic record
Abstract
NRC-CISTI serves Canada as its National Science Library (as mandated by Canada's Parliament in 1924) and also provides direct support to researchers of the National Research Council of Canada (NRC). By reason of its mandate, vision, and strategic positioning, NRC-CISTI has been rapidly and effectively mobilizing Canadian stakeholders and resources to become a lead player on both the Canadian national and international scenes in matters relating to the organization and management of scientific research data. In a previous communication (CODATA International Conference, 2008), the orientation of NRC-CISTI towards this objective and its short- and medium-term plans and strategies were presented. Since then, significant milestones have been achieved. This paper presents NRC-CISTI's most recent activities in these areas, which are progressing well alongside a strategic organizational redesign process that is realigning NRC-CISTI's structure, mission, and mandate to better serve its clients. Throughout this transformational phase, activities relating to data management remain vibrant.
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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.046 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.010 |
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".