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Record W2897029359 · doi:10.2118/191589-ms

Case Study: Apache's Approach to Societal Responsibility thru the Environmental Media Monitoring Program for the Alpine High Play

2018· article· en· W2897029359 on OpenAlexaff
Navneet Behl, Brian Bohm, Marcus Bruton, Steven D. Fleming, S. Vance

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsBaseline (sea)Environmental dataEndangered speciesNatural (archaeology)CorporationEnvironmental impact assessmentGroundwaterEnvironmental monitoringEnvironmental scienceStructural basinEnvironmental resource managementNatural resourceGeographyBusinessEngineeringGeologyEnvironmental engineeringEcologyArchaeologyOceanography

Abstract

fetched live from OpenAlex

Abstract A baseline environmental media monitoring program is presented. Analytical data sets collected by third party contractors at the request of Apache Corporation are presented. The analytical data presented documents the environmental condition of groundwater, surface water, soil, and ambient air, within an area with sensitive environments supporting multiple endangered and threaten species, recently being developed as oil and natural gas exploration and early development. The area of the study is a previously undeveloped portion of the prolific oil and gas development area of the Permian Basin. The baseline environmental media monitoring program occurred over an area of approximately 78 square miles in southern Reeves County, Texas. The analytical data provides interested stakeholders a starting data set for comparison to future environmental conditions, allowing parties to assess if there are changes in the environmental media and if those changes vary from natural seasonal variations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.066
GPT teacher head0.362
Teacher spread0.296 · 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 designQualitative
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

Citations2
Published2018
Admission routes1
Has abstractyes

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