Bibliographic record
Abstract
The National DNA Data Bank of Canada (NDDB) began operations on June 30, 2000 and started receiving convicted offender samples and crime scene DNA profiles. As of March 31, 2006, the NDDB has provided investigative leads in over 4,900 cases, matching DNA profiles from convicted offenders to DNA profiles from biological evidence from crime scenes. The cases assisted range from the simplest break and enter cases to some very complex high profile murder and sexual assault cases ranging in age from recent to cold cases over twenty-one years old. A history of the development of the NDDB is provided along with an update of its current activities. The NDDB has grown into a world-class forensic DNA data bank and continues to be innovative in developing new processes.
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 | 0.039 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".