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Record W2282906829 · doi:10.5539/mas.v10n4p1

Study Clastic Sediments and Evaporite Deposits’ Changes in the Sedimentary Core Lake Maharlou, Iran

2016· article· en· W2282906829 on OpenAlexvenueno aff
Abdolah Safe, Fatemeh Sabokkhiz, Mohamad Hosein Ramesht, Morteza Djamali, Abdolmajid Naderi Beni

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporiteGeologyHaliteClastic rockSedimentary rockGypsumGeochemistryAnhydriteSiltCalciteLoss on ignitionDeposition (geology)CarbonateMineralogySedimentGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

The continental environments, lakes are proper for deposition locations of evaporites. Evaporite minerals are formed wherever the evaporation rate is more than incoming water to the basin. In this article the evaporate deposits (Calcite, Gypsum and Halite) are studied in a sedimentary core of Lake Maharlou, Zagros Mountains, South of Iran. The core sample treated for getting Magnetic Susceptibility values along with the core as well as basic sedimentological data including grain size, Total Organic Matter and carbonate contents. NaCl is determin ed by gravimetric analysis. Loss on Ignition is applied to measure and estimate the amount of (OC), (Ca) and (SO4) mineralogy of which is determined by SEM method. The exists a direct relation between evaporation deposit formation of lake water level reduction. Accordingly, the change in the sediment stratum indicating the level of evaporations. The results indicate a lower extant of gypsum than Ca and NaCl. The sequence of layers principle, changes in the shoreline (lake water level fluctuations) with respect to stratum zonation. Magnetic susceptibility level is directly related to the Silt layer thickness but also there is an indirect relation with the level of organically rich sediments’ occurrence and abundance.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.248
Teacher spread0.217 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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