INNOVATIVE, COLLABORATIVE RESEARCH ON SPECIMENS FROM THE NATIONAL METEORITE COLLECTION OF CANADA
Bibliographic record
Abstract
Summary The National Meteorite Collection of Canada (NMTC), housed within Natural Resources Canada, Earth Sciences Sector, Ottawa, includes 2700 fragments and masses of 1100 different meteorites. Specimens from the collection have been utilized by local scientists and engineers for hardware, software, analytical and method development related to space exploration. This has led to a recognition of the utility of meteorites to help build a larger skilled community of planetary scientists in Canada in anticipation of manned and robotic missions to the Moon, Mars, asteroids and comets, and sample returns from there. The successful collaboration can be linked to initiatives from the NMTC, Carleton University (CU) and the Neptec Design Group, a NASA prime contractor, since 2002. These include: Use of 3D laser imaging of terrestrial rock samples, and meteorites, to help develop image libraries for use in recognition of the nature of solid objects in extraterrestrial environments (meteorites and igneous, sedimentary and metamorphic rocks as outcrops or boulders) (Herd et al. 2003). Non-destructive measurement of petrophysical properties of meteorites especially volume/density and magnetic susceptibility. The first digitally determined meteorite volume was achieved along with ways to use the magnetic response of stony meteorites to help in their classification (Smith et al. 2006a,b).
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".