Structure and data quality assessment of the Kimberlite Indicator and Diamond Database (KIDD)
Bibliographic record
Abstract
The Kimberlite Indicator Diamond Database (KIDD) developed by Northwest Territories Geoscience Office is a relational database archiving kimberlite indicator mineral (KIM) grain counts reported within assessment reports of mining activity in the Northwest Territories and Nunavut. KIDD archives four main types of sample data and metadata: 1) sample and site descriptions (general sample attributes: sample number, location, etc.); 2) information on KIM (grain count data for suites of KIM); 3) analytical information (processing techniques, processed size fractions, etc.); 4) comments (mixed array of sample site attributes, analytical information, KIM information). The completeness and accuracy of reported KIM grain counts is variable. There are KIM grain count entries for only ~34% of all archived samples. Entries for individual KIM grain counts vary between ~0.02% (diamonds) to ~16% (garnets). Despite some limitations in grain count reporting, the data contained within KIDD are of high quality and integrity: 87-100% of data reported within assessment reports are faithfully and accurately reported within KIDD. Limitations to the use of KIDD also result from irregular and non-standardized inconsistent reporting of sample weights and sampling site attributes. These limitations result not from an absence of data but from the database structure itself that does not contain proper fields for standardized reporting of these attributes. Overall, KIDD offers a useable archival dataset of high data quality. However, numerous caveats and limitations in data reporting require careful data evaluation by the user.
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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 | 0.054 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".