MétaCan
Menu
Back to cohort
Record W7135612545

Application of fluid inclusions in the study of hydrothermal ore deposits

2025· dissertation· cs· W7135612545 on OpenAlexaboutno aff
Daria Mieliekhova

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2025
Typedissertation
Languagecs
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFluid inclusionsHydrothermal circulationMineralization (soil science)Ore genesisSalinityParagenesis
DOInot available

Abstract

fetched live from OpenAlex

This bachelor thesis represents a review of microthermometric studies of fluid inclusions and their use in interpreting the formation and evolution of hydrothermal ore deposits. The aim is to demonstrate how cryometric and homogenization temperature data from inclusions can be used to reconstruct mineralization formation conditions. The thesis is subdivided into two parts. The theoretical part describes the origin, classification and thermodynamic behavior of inclusions, including a basic overview of the H2O - NaCl, H2O - CO2 a H2O - NaCl - CO2 systems. The second part is a literature-based review and includes 12 selected ore deposits of different genetic types (magmatic, post-magmatic, metamorphic, etc.), both from abroad (Zimbabwe, France, Canada, South Africa) and from the Czech Republic (Mokrsko, Horní Luby, Nízký Jeseník, Prague Synform). For each locality, geology, mineralogy, and fluid type(s)s are described. Based on published microthermometric data, the tempetures, salinity and composition of fluids are compared/discussed. In several cases, multistage mineralization and/or later fluid overprint(s) were identified. The thesis shows that fluid inclusions often record multiple stages in the evolution of ore-forming systems and reflect mixing of fluids of different origins (magmatic,...

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.628
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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.

Study designObservational
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueDigital Repository (National Repository of Grey Literature)Same topicGeothermal Energy Systems and ApplicationsFrench-language works237,207