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Record W4232674045 · doi:10.2138/gselements.14.6.432

Mineralogical Association of Canada

2018· article· en· W4232674045 on OpenAlexfundaboutno aff

Bibliographic record

VenueElements · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMineralogy and Gemology Studies
Canadian institutionsnot available
FundersUniversity of WaterlooUniversity of TorontoQueen's UniversityDalhousie UniversityMount Royal UniversityLakehead UniversityUniversité du Québec à ChicoutimiBrock UniversityAcadia UniversityUniversité du Québec à MontréalUniversity of WindsorUniversity of ReginaMcGill UniversityUniversity of AlbertaUniversité Laval
KeywordsGeologyAssociation (psychology)ArchaeologyGeographyPhilosophy

Abstract

fetched live from OpenAlex

The latest thematic issue of The Canadian Mineralogist comprises contributions from the 8 th International Symposium of Granitic Pegmatites (PEG2017), which was held in Kristiansand (Norway).The issue is dedicated to Milan Novák, a distinguished Czech Republic researcher in the field of pegmatite mineralogy and petrology.Milan spent his research career at two institutions.Between 1977 and 1999, he worked at the Moravian Museum in Brno (Czech Republic) as a curator of the mineralogical collections, rising to become Head of the Department of Mineralogy and Petrology.Between 1999 and 2003, he was employed at Masaryk University in Brno as Director of the Department of Mineralogy, Petrology and Geochemistry.After the merging of two geological departments at the university, he was Director of the Department of Geological Sciences (2003-2007; 2016-2017) until his partial retirement in 2017.His research in the field of granitic pegmatites benefited from his early experiences of collecting and field surveying for the Moravian Museum and from his teachers during the 1970s and 1980s:

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.255
Threshold uncertainty score0.853

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0060.001
Scholarly communication0.0060.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.2550.097

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 head0.009
GPT teacher head0.201
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes2
Has abstractyes

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