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Record W4281986556 · doi:10.1071/aj21469

APPEA Panel Series. Challenges and opportunities for offshore oil and gas decommissioning in Australia

2022· article· en· W4281986556 on OpenAlexaff
David Maxwell, Ursula Benson, Stuart Barrymore, Cameron Grebe, S. W. Gaddes, Jarrod Pittson

Bibliographic record

VenueThe APPEA Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsSmiths Detection (Canada)
Fundersnot available
KeywordsNuclear decommissioningLegislationPetroleum industryBusinessGovernment (linguistics)Submarine pipelinePetroleumEngineeringWaste managementPolitical scienceLawGeologyEnvironmental engineering

Abstract

fetched live from OpenAlex

Concurrent 16. Wednesday 18 May Australia’s offshore petroleum decommissioning regulatory regime has recently seen significant changes with new, far reaching changes to legislation affecting decommissioning recently passed. More change is yet to come, with a shake up of decommissioning reporting requirements and pending, more stringent obligations on industry for financial assurance. Leading industry, legal, government and regulators discuss these key issues affecting petroleum decommissioning in Australia today and what it means for the industry in the future. To view the video, click on the link to the right.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0410.007

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 head0.091
GPT teacher head0.251
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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