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New computer program TecD for tectonomagmatic discrimination from discriminant function diagrams for basic and ultrabasic magmas and its application to ancient rocks.

2013· article· en· W1999715720 on OpenAlexaboutno aff
Surendra P. Verma, M. Abdelaly Rivera‐Gómez

Bibliographic record

VenueJournal of Iberian Geology · 2013
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
FundersConsejo Nacional de Ciencia y Tecnología
KeywordsGeologyUnavailabilityDiscriminantArcheanTernary plotInterculturalityIgneous rockTectonicsComputer scienceGeochemistrySeismologyArtificial intelligenceMathematicsStatisticsProgramming languageHumanities

Abstract

fetched live from OpenAlex

Due to the unavailability of suitable software, a new computer program TecD (Tectonomagmatic Discrimination) was written in Visual Basic for using four sets of new discriminant function based diagrams published during 2004-2011. This bilingual (both in English and Spanish) program evaluates igneous rock geochemistry data in 20 different multi-dimensional diagrams (4 sets of 5 diagrams each), automatically counts the number of samples plotting in different tectonic fields, computes success rates (%) for a given area or set of rock samples, provides scalable vector diagrams that can be opened and modified in different commercial software, and presents a synthesis of this application in a final report. Three examples are presented to highlight the use of TecD. Ocean island setting is inferred for ~56 Ma basic rocks from Faroe Islands (Atlantic Ocean), mid-ocean ridge for ~2700 Ma Archean Abitibi greenstone belt (Canada), and arc setting for ~2950 Ma Mallina basin (Australia). Additional criteria for the interpretation of these diagrams are also briefly discussed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.483

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 science0.0000.000
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 head0.011
GPT teacher head0.229
Teacher spread0.219 · 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 designOther design
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

Citations24
Published2013
Admission routes1
Has abstractyes

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