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Record W3118506199 · doi:10.2138/am-2021-7590

A comment on “An evolutionary system of mineralogy: Proposal for a classification of planetary materials based on natural kind clustering”

2020· article· en· W3118506199 on OpenAlexaff
Frédéric Hatert, Stuart J. Mills, F. C. Hawthorne, Michael S. Rumsey

Bibliographic record

VenueAmerican Mineralogist · 2020
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNomenclatureMineralNatural (archaeology)Natural mineralAstrobiologyCluster analysisClassification schemeBiological classificationEarth scienceGeologyMineralogyComputer scienceTaxonomy (biology)Artificial intelligenceInformation retrievalBiologyPaleontologyEcologyEvolutionary biology

Abstract

fetched live from OpenAlex

Abstract The classification and nomenclature of mineral species is regulated by the Commission on New Minerals, Nomenclature and Classification of the International Mineralogical Association (IMACNMNC). This mineral species classification is necessary for Earth Sciences, as minerals constitute most planetary and interstellar materials. Hazen (2019) has proposed a classification of minerals and other Earth and planetary materials according to “natural clustering.” Although this classification is complementary to the IMA-CNMNC mineral classification and is described as such, there are some unjustified criticisms and factual errors in the comparison of the two schemes. It is the intent of the present comment to (1) clarify the use of classification schemes for Earth and planetary materials, and (2) counter erroneous criticisms or statements about the current IMA-CNMNC system of approving proposals for new mineral species and classifications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations12
Published2020
Admission routes1
Has abstractyes

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