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Record W3035590161 · doi:10.1144/geochem2020-017

Ammonium abundance and short-wave infrared absorption spectra of altered rocks

2020· article· en· W3035590161 on OpenAlexafffund
Kéiko Hattori, A.Amiero Fonseca, Tabetha Sheppard

Bibliographic record

VenueGeochemistry Exploration Environment Analysis · 2020
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbundance (ecology)AmmoniumInfrared spectroscopyInfraredAbsorption (acoustics)Analytical Chemistry (journal)ChemistrySpectral lineEnvironmental chemistryMaterials sciencePhysicsBiologyEcologyOpticsAstronomyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Rhyolitic rocks hosting the El Zapote epithermal Ag deposit are pervasively altered and contain ammonium ranging from 290 to 1750 ppm. High ammonium values are found in samples containing abundant fine-grained illite. This fine-grained illite shows overall low ratios of K/(Al + Fe), likely due to ammonium substituting K + . Samples containing high ammonium, greater than 1000 ppm in the proximity of Ag-bearing veins, show distinct absorption features of ammonium in short-wave infrared (SWIR) absorption spectra. Samples containing ammonium ranging from 500 to 1000 ppm show mixed absorption signatures; some show prominent absorption features related to ammonium, whereas others have no recognizable features. There is no discernible absorption feature related to ammonium for samples containing less than 500 ppm NH 4 . The data suggest that SWIR spectroscopy is useful in locating the proximal areas to mineralization, but the extent of ammonium alteration is much larger than that identified by SWIR spectroscopy. Nitrogen isotope compositions of ammonium are similar to those of sedimentary rocks, suggesting that the source of ammonium in altered rhyolite is sedimentary basement rocks in the area. Supplementary material : bulk rock composition is available at https://doi.org/10.6084/m9.figshare.c.5015663

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.831

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 head0.028
GPT teacher head0.202
Teacher spread0.174 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes2
Has abstractyes

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