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Record W2559409519 · doi:10.4043/27393-ms

Optimizing Geochemical and Sediment Sampling in Frontier Areas by Reviewing Past Projects and Analyzing the Benefits of Introducing New Technologies and Practices

2016· article· en· W2559409519 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNalcor Energy (Canada)
Fundersnot available
KeywordsCoringBathymetrySampling (signal processing)Submarine pipelineCore samplePetroleumComputer scienceEnvironmental scienceSample (material)Core (optical fiber)GeologyEngineeringOceanographyDrillingTelecommunications

Abstract

fetched live from OpenAlex

Abstract Due to the large geographical areas typically associated with frontier regions, the need to enhance techniques for gathering information on petroleum systems and prospect charge is critical to ensure efficient and accurate results. Small changes in efficiencies of acquisition techniques and use of ever improving technology can significantly improve the chances of success. The results from a 2015 geochemical sampling program offshore Labrador and Newfoundland were reviewed, to determine areas of improvement and efficiency for future work in this and other frontier areas. Seismic data acquired offshore Newfoundland and Labrador plus surficial satellite seep mapping hinted at active petroleum systems and thus a need for a geochemical survey to assess these was determined. Evaluated areas were divided into two groups, one to identify regional petroleum systems and the other to reduce prospect charge risk. Core samples, heatflow and bathymetry were among the data collected. Using the concept of 'intelligent sampling' the authors are developing systems and procedures to ensure efficiency and improve the chances of analytical success. Dedicated vessels with DP systems are utilized, complete with a full range of multi-beam sonars, sub-bottom profilers, dedicated launch and recovery systems, and sub-surface positioning to ensure coring accuracy. Further innovations included core barrel mounted cameras and coring rope load monitoring. Based on the 2015 survey, modified approaches are proposed to improve sample acquisition, many of which are being implemented in the 2016 survey:core recovery (ensure cores penetrate below the biogenic zone) with revisions to the drop core assembly design;evaluation of appropriate coring methods (gravity, piston and vibro);new technologies for live slick sampling (traditionally difficult in areas of rough weather or sea conditions) are analysed with oil detection radars and seaborne/airborne drones;methods to reduce probability of sample contamination; and,best practice storage methods to meet the needs of the variety of analytical methods proposed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.398

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.018
GPT teacher head0.237
Teacher spread0.218 · 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