Effective project-level information management: development of a standardized protocol for capturing metadata within the Baffin Bay Basins Project of the GEM-Energy Program
Bibliographic record
Abstract
Although often neglected, the generation of metadata is a critical component of any project information management process. Ideally, a corporate standard should define the type of metadata content to be collected for all project-related geospatial files. In practice, metadata collection is typically ad hoc and up to individual practice and preference. The authors present a simple method by which metadata can be easily initiated by the project participant in the course of generating geospatial data files. The original data files with the associated metadata files are then transferred to the project information management leader for vetting and long-term archiving. Perception of metadata generation, and its general acceptance of value by working-level scientists, can benefit from simply defined practices that can be easily executed. The authors' methodology is a relatively simple and streamlined implementation process that meets the authors' information management duty to create metadata, while exploring practical and understandable procedures, and is reflected in a real, project-level case study. The methodology and procedures can stand for consideration within the broader long-term determination of best practices or development of standards for information management within Natural Resources Canada.
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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 | 0.075 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".