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Record W2123861587 · doi:10.5430/bmr.v2n2p96

A Brief Analysis of Low-Carbon Agriculture Development Pattern

2013· article· en· W2123861587 on OpenAlexvenueno aff
Meijuan Kang

Bibliographic record

VenueBusiness and Management Research · 2013
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureCarbon fibersBusinessGreenhouse gasNatural resource economicsLow-carbon economySustainable Agriculture Innovation NetworkClimate changeEnvironmental scienceEconomicsComputer scienceEcology

Abstract

fetched live from OpenAlex

Low-carbon agriculture demonstrated mild economy in the development of agriculture in which it different from ecotype agriculture, environmental agriculture and circulatory agriculture. Low-carbon agriculture is energy saving technology, solid carbon technology and it is also the reproducible agriculture recommended in the agriculture field in order to maintain the global environment safety and improves global climate. Low energy, low releasing, low pollute are the characteristics for the Low-carbon agriculture .It is new type agriculture with multi-functions such as: agriculture industry, safe security, climate adjustment, environment restraint and countryside finance. Development of low-carbon agriculture is an urgent need and great potential and promising. Low energy, low releasing, low pollute and high capability ,high efficiency, high profitability(three low or high) is the base for the Low-carbon agriculture develop mode .To finally increasing the farmer income and To improve agriculture efficiency and advance economy we have to defined the direction with Low-carbon ,use energy saving and solid carbon development as tool .

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.008
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.002

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 head0.093
GPT teacher head0.385
Teacher spread0.292 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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