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Record W4280556442 · doi:10.1071/aj21192

INPEX-led Ichthys Joint Venture – developing a dynamic adjustment area to identify, time, duration and potential exposure periods related to a 2D seismic survey offshore Western Australia

2022· article· en· W4280556442 on OpenAlexaff
Jake Prout, Paul D. Miller, Shane O’Donoghue

Bibliographic record

VenueThe APPEA Journal · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsDillon Consulting
Fundersnot available
KeywordsSubmarine pipelineSurvey data collectionEnvironmental scienceGeographyGeologyOceanography

Abstract

fetched live from OpenAlex

The oil and gas industry is striving for effective consultation between titleholders and commercial fishing stakeholders. Recent developments in cross-industry consultation have resulted in frameworks to provide an adjustment (compensation) claim process, where short-term displacement of fishers and reduction of catch coincident and following marine seismic survey activities may occur. 3D seismic surveys are undertaken more intensively, in geographically smaller areas and shorter time periods. This allows for discreet areas and periods to be defined for the applicability of a claim when fishing catch rates or displacement may occur. By contrast, 2D seismic surveys can be undertaken at regional scales with long sail line transects. These result in a survey vessel being active in an area of potential acoustic influence for infrequent and short periods of time, thus making justification for claims, for loss of catch or displacement, over the full survey area and full acquisition period challenging. To address this challenge a novel approach was developed to determine a dynamic area, based on where and when a survey vessel was operating the seismic source. INPEX utilised the 10 nautical miles (nm) × 10 nm geospatial grid data set provided by Department of Primary Industries Resources and Development, for commercial fishers to report catch effort. The methodology used modified navigation files, to determine the frequency, duration and number of times a fishing block was intersected during the survey. The resulting data provided an ‘adjustment area’ and ‘exposure date’ which could then be referred to as a basis for claim.

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 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 score0.353
Threshold uncertainty score0.996

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.284
Teacher spread0.261 · 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
Published2022
Admission routes1
Has abstractyes

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