Evaluation of NRC Measurement Science and Standards
Bibliographic record
Abstract
This report presents the results of the 2014‑15 evaluation of the National Research Council (NRC) Measurement Science and Standards (MSS) Portfolio. MSS is Canada's national metrology institute (NMI), conducting research and providing primary metrology services in the national interest. In this role, MSS provides traceability to the International System of Units (the SI, or metric system) for Canada and supports Canada's participation in the Bureau international des poids et mesures (BIPM). The Portfolio hosts three programs: Metrology for Industry and Society (MIS), Measurement Science for Emerging Technologies (MSET), and Scientific Support for the National Measurement System (SSNMS). Together, MSS activities aim to improve social and economic prosperity by enabling both product and process innovation in areas where precise and reliable measurements are critical to success.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Other About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
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 | 0.355 | 0.453 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.031 | 0.031 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.032 | 0.006 |
| Open science | 0.021 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".