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Record W4404905628 · doi:10.1016/j.renene.2024.121953

Blue vs. Green: A comparative analysis of ammonia production and export in Western Canada and Australia

2024· article· en· W4404905628 on OpenAlexaffabout
Julian Palandri, Hamid Rahmanifard, David B. Layzell, Sara Hastings‐Simon

Bibliographic record

VenueRenewable Energy · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProduction (economics)AmmoniaAmmonia productionEnvironmental scienceAgricultural economicsEconomicsChemistryMacroeconomics

Abstract

fetched live from OpenAlex

In a net-zero future, the global trade of traditional hydrocarbons will decline, increasingly replaced with the international transport of energy carriers that generate no greenhouse gas (GHG) emissions. Ammonia is a zero-emission energy carrier that could play a significant role in providing energy to countries like Japan, which are challenged to meet their own energy demands. Canada and Australia are among the nations that have the potential to produce cost effective low GHG ammonia for export. Emission free electricity from Australia’s solar and wind resources could be used to produce ‘green’ ammonia. In Canada, natural gas could be reformed to ‘blue’ ammonia if the byproduct CO2 is captured and sequestered. This study uses techno-economic tools to compare the cost of ammonia production and transport to Japan from either Canada or Australia in 2020, 2030 and 2050.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.243
Teacher spread0.224 · 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 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

Citations8
Published2024
Admission routes2
Has abstractno

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