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Record W2750330405 · doi:10.1071/aj08050

Coal seam gas in Australia’s progression to a low carbon economy*

2009· article· en· W2750330405 on OpenAlexaff
Andrew Petersen, Hugh McKerrow

Bibliographic record

VenueThe APPEA Journal · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsClimate changeGreenhouse gasClimate change mitigationNatural resource economicsEnergy supplyEmissions tradingRenewable energyLow-carbon economyCoalBusinessCarbon capture and storage (timeline)Energy securityEconomicsEnergy (signal processing)EngineeringWaste management

Abstract

fetched live from OpenAlex

The previous decade has witnessed an unprecedented increase in societal appreciation for the existence of climate change and its associated impacts. One need only look to the reports of the Intergovernmental Panel on Climate Change (IPCC) for evidence—between 1990 and 2007, scientific acceptance of the anthropogenic nature of climate change has risen from merely perhaps in 1990 to a certainty of 90% in 2007. As governments look to create imposts on the very emissions causing climate change, be it through emissions trading schemes (ETS) or through carbon taxes, an equally stark appreciation has occurred in relation to the need to switch to low emissions fuel source in the absence of carbon capture and storage. In contrast to the introduction of the EU ETS, fuel switching in Australia will be more problematic—now Australia sources only a small fraction of its energy supply from renewable energy sources and it will take some time for this to change. What is therefore needed, is a transition fuel—a fuel that will provide Australia with a stepping stone to a sustainable future while at the same time ensuring the security of our energy supply. Coal seam gas (CSG) could play an important part in this progression. Its role is not, however, without its complexities. In a world of daily regulatory and market developments, the CSG industry will need to incorporate both Australian and international climate change issues into its development plans—its physical, reputational, regulatory and market risks and opportunities. This extended abstract will examine the links between these exposures and the future growth potential of the industry.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.008
GPT teacher head0.241
Teacher spread0.233 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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