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Record W3125670508

CO2 Emissions from Central Canadian Agriculture: Meeting Kyoto Targets and Its Implications

2006· article· en· W3125670508 on OpenAlexaboutno aff
Varghese Manaloor

Bibliographic record

Venue2006 Annual Meeting, August 12-18, 2006, Queensland, Australia · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolAgricultureTonneGreenhouse gasAgricultural economicsFossil fuelNatural resource economicsCarbon taxEnvironmental scienceProductivityEconomicsBusinessGeographyEngineeringWaste managementEcologyEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

Agriculture sectors dependence on fossil fuel use (both direct and indirect) has increased dramatically over the past decades. Productivity increases have been achieved using technological improvements which use considerable amounts of energy inputs. Concerns about global environmental quality resulted in several countries signing the Kyoto protocol, which came into effect internationally, on February 16, 2005. Canada has made a commitment to the international community to stabilize CO2 emissions at 6 percent below 1990 levels. The target is supposed to be reached by 2008 and maintained through 2012. This paper estimates the CO2 emissions from input use in Central Canadian agriculture. Using elasticity estimates, the amount of price increase needed to achieve Kyoto targets is estimated. A 6 percent reduction from 1990 levels implies that CO2 emissions should be stabilized at 1, 424, 562 tonnes of carbon. The removal of current provincial farm fuel tax exemption programs will lead to a decrease of only 3.36 percent reduction in CO2 emissions and is estimated to be at 1, 726, 356 tonnes of carbon. Fuel prices will have to increase almost 85 percent in order to achieve the target reductions under the Kyoto agreement.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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Same venue2006 Annual Meeting, August 12-18, 2006, Queensland, AustraliaSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207