MétaCan
Menu
Back to cohort
Record W1548285381

Ensuring Canadian Access to Oil Markets in the Asia-Pacific Region

2012· article· en· W1548285381 on OpenAlexaffabout
Gerry Angevine, Vanadis Oviedo

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldEnergy
TopicRenewable energy and sustainable power systems
Canadian institutionsFraser Institute
Fundersnot available
KeywordsOil refineryAsphaltOil sandsCrude oilPipeline transportBusinessPetroleumPort (circuit theory)ChinaProduction (economics)Natural resource economicsAgricultural economicsEnvironmental scienceEngineeringGeographyWaste managementEconomicsPetroleum engineeringEnvironmental engineering
DOInot available

Abstract

fetched live from OpenAlex

Demand for oil products in countries in the Asia-Pacific region is rapidly increasing and crude oil can be sold in those markets at a premium. If Canadian oil producers had access to the US and the Asia-Pacific region, they could secure the best possible return on their investment. This would also reduce the risk that growth in Canadian oil production could be constrained by US opposition inhibiting the construction of new pipelines. Construction and operation of pipelines from Alberta to ports in British Columbia could contribute substantially to GDP, and to employment and income in Alberta, British Columbia, and the rest of Canada. Additionally, the possible price premiums on sales to markets in the Asia-Pacific region would benefit the shareholders of the oil production companies, including many public and private pension funds. Outdated regulatory processes and procedures, First Nations ’ opposition, and unwieldy environmental review processes are impeding the timely development of the infrastructure required to transport oil to the west

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)0.002
metaresearch head score (Gemma)0.006
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 score0.033
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0090.002
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.002

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 head0.012
GPT teacher head0.235
Teacher spread0.222 · 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

Citations3
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicRenewable energy and sustainable power systemsFrench-language works237,207