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Record W7062076893

Short Wavelength Infrared Spectral Characteristics of the HW Horizon:
\nImplications for Exploration in the Myra Falls Volcanic-Hosted Massive Sulfide Camp, Vancouver Island, British Columbia, Canada

2005· article· en· W7062076893 on OpenAlexaboutno aff

Bibliographic record

VenueUTAS Research Repository · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsnot available
Fundersnot available
KeywordsMuscoviteMicaChloriteHydrothermal circulationWavelengthSulfideAbsorption (acoustics)RhyoliteSericite
DOInot available

Abstract

fetched live from OpenAlex

Short wavelength infrared (SWIR) spectrometry has been used to identify previously unmapped hydrothermal
\nalteration zones around volcanic-hosted massive sulfide (VHMS) orebodies at Myra Falls, Vancouver Island,
\nBritish Columbia. Hydrothermal alteration assemblages are uniformly dominated by fine-grained white
\nmica, with poor development of mineralogical zonation. SWIR spectrometry is an ideal exploration tool for
\ncharacterizing this fine-grained hydrothermal alteration. At Myra Falls, SWIR spectrometry has identified subtle
\nshifts in the wavelengths of the AlOH absorption feature of white mica, corresponding to compositional
\nchanges in altered rhyolite distal and proximal to ore. AlOH absorption occurs at shorter wavelengths (<2,198
\nnm) and corresponds to lower Fe, Fe + Mg, and Si/Al and higher Na/(Na + K) in strongly altered samples proximal
\nto ore (slightly sodic muscovites). AlOH absorption occurs at longer wavelengths (>2,206 nm) and corresponds
\nto higher Fe, Fe + Mg, and Si/Al and lower Na/(Na + K) in samples distal to ore (nonsodic slightly
\nphengitic muscovites). White mica in siltstone within a meter of VHMS ore has higher Zn, V, Fe, and Mg contents
\nthan white mica distal to these altered samples. Chlorite compositions, identified by SWIR, also show systematic
\nchanges with intensity of alteration and distance from ore. The average wavelength of the FeOH absorption
\nfeature for chlorite in rhyolitic samples proximal to ore is 2,241 nm (intermediate Mg chlorite),
\nwhereas wavelengths in background samples average 2,247 nm (intermediate Fe chlorite). Similar changes are
\nobserved in footwall and hanging-wall andesites, with samples near the Battle mine containing muscovite to
\nphengitic muscovite (average wavelength of the AlOH absorption feature of 2,200 nm) and Mg-rich chlorite
\n(average wavelength of the FeOH absorption feature of 2,245 nm) to regional andesite samples with phengitic
\nmuscovite (average wavelengths of the AlOH absorption feature of 2,209 nm) and Fe-rich chlorite (average
\nwavelength of the FeOH absorption feature of 2,249 nm). In weakly altered rocks white mica compositions also
\nvary with host lithology. The AlOH absorption feature occurs at longer wavelengths in white mica in dacite and
\nandesite compared to adjacent rhyolitic rocks, suggesting that higher Fe and Mg in the host lithology affects
\nthe composition of white mica.
\nTwo zones of intense hydrothermal alteration above the Battle and HW orebodies have distinctive SWIR
\nspectral characteristics, with the AlOH and FeOH features occurring at shorter wavelengths (<2,197 and
\n<2,240 nm, respectively). Small anomalous zones of alteration were also identified in the Thelwood Valley area,
\nwhere minor mineralized zones are present. As broad zones of fine-grained white mica (sericite) alteration are
\nubiquitous throughout the Myra Falls property, alteration proximal to ore cannot be identified simply by visual
\nlogging of drill core. Alteration zonation may be determined by subtle shifts in white mica spectral characteristics.
\nThis study indicates that SWIR analysis may be an effective field-based exploration tool for quantifying
\nthe intensity of alteration associated with VHMS orebodies, and that trends in mineral compositions, even in
\nvery fine grained rocks, can be used as mine-scale vectors to ore.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.551

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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 designNot applicable
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
Published2005
Admission routes1
Has abstractyes

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