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Record W138244815

INNOVATIVE, COLLABORATIVE RESEARCH ON SPECIMENS FROM THE NATIONAL METEORITE COLLECTION OF CANADA

2010· article· en· W138244815 on OpenAlexaboutno aff
R. K. Herd, C. Samson, I. Christie

Bibliographic record

VenueEspace ÉTS (ETS) · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
Fundersnot available
KeywordsMeteoriteExtraterrestrial lifeAstrobiologyGeologyMars Exploration ProgramPetrophysicsEarth sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.286
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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