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Record W4385561045 · doi:10.18411/trnio-12-2022-404

Environmental Impacts of chemical composition Produced Water (оn the Siyazan field example)

2022· article· en· W4385561045 on OpenAlex
N.I. Mammadova

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsImpact
Fundersnot available
KeywordsEnvironmental scienceEnvironmental chemistryPollutionChemical compositionComposition (language)Fossil fuelProduced waterHydrology (agriculture)GeologyEnvironmental engineeringChemistryEcology

Abstract

fetched live from OpenAlex

This article describes the increased environmental impact of fuel energy complex depending on growth in oil and gas production. At the moment, due to the intensification of oil and gas production, associated mineralized reservoir water is one of the main sources for environmental pollution, and sufficiently plays role at soil cover degradation. This paper reviews recent research analysis of the physical chemical composition of formation water and the effect of this composition on soil ecosystems on the example of monocline of Siyazan field. Water samples have been taken from of monocline of Siyazan field. This information has been used to determine the most significant contaminants and their geochemical behaviour. Based on the analysis results, the examined formation water samples were characterized in terms of various classifications used in oil and gas hydrogeology. And, conclusions were drawn and it was noted that the hydrochemical composition of mineralized reservoir waters to degradation of biocenoses and soil cover.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.984

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.017
GPT teacher head0.176
Teacher spread0.159 · 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