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Record W2763930136 · doi:10.18551/rjoas.2017-09.02

ECONOMIC ANALYSIS OF THE TECHNICAL EQUIPMENT OF AGRICULTURE, THE CURRENT MECHANISMS FOR REGULATING THE AGRICULTURAL MACHINERY MARKET IN CANADA AND ASSESSING THE POSSIBILITY OF THEIR APPLICATION IN RUSSIA UNDER CONDITIONS OF IMPORT SUBSTITUTION

2017· article· en· W2763930136 on OpenAlexaboutno aff
Аndrey А. Polukhin

Bibliographic record

VenueRussian Journal of Agricultural and Socio-Economic Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Development and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureBusinessNatural resource economicsAgricultural economicsIndustrial organizationInternational tradeEconomicsGeography

Abstract

fetched live from OpenAlex

АННОТАЦИЯ В статье проведена сравнительная оценка технической оснащенности сельского хозяйства Канады.Сравнение проведено со странами с сопоставимыми по размеру сельскохозяйственных угодий, климатическими условиями.Проанализировано изменение технической оснащенности сельского хозяйства Канады как в количественном, так и в стоимостном выражении.В статье дана структурный анализ машинотракторного парка сельского хозяйства Канады.В качестве источника эмпирического материала использованы данные официальной статистики Канады.На основе результатов экономической оценки технической оснащенности сельского хозяйства Канады сделаны выводы, которые можно использовать в качестве обоснований направлений развития сельского хозяйства как изучаемой страны, так их экстраполировать на страны со схожими условиями аграрного производства.

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 score0.444
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.265
Teacher spread0.246 · 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
Published2017
Admission routes1
Has abstractyes

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