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Record W2990261223

ANALYSIS OF EFFICIENCY INDICATORS OF FLAX FOR OIL CULTURES

2019· article· ro· W2990261223 on OpenAlexaboutno aff
Ioana Anda Milin, Iuliana Ioana Merce, Cornelia Petroman, Elena Peț, Remus Gherman

Bibliographic record

VenueLucrări Științifice, Universitatea de Științe Agricole Și Medicină Veterinară a Banatului, Timisoara, Seria I, Management Agricol · 2019
Typearticle
Languagero
FieldBusiness, Management and Accounting
TopicVaried Academic Research Topics
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyChinaEuropean unionCompetition (biology)RomanianAgricultural economicsOil productionCropOlive oilEconomyBusinessInternational tradeEconomicsForestryBiologyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The origin of area of the flax for oil is considered to be the center and west of Asia, from where it spread to the west and north. At present, about 50% of the area cultivated with oil flakes is in Asia (India being the largest cultivator, followed by China), about 25% of the cultivated area is in North America (Canada). Europe grows flax  for oil in UK, France, Russian Federation, Belarus and Ukraine (which are seriously investing in the production of linseed  for oil, which they export to Europe). The causes that hinder the expansion of culture in European countries are low production and inconsistency ,as well as strong competition from Canada's seed imports. The European Union is the largest consumer of seeds and flax for oil. In Romania, in recent years, the interest of farmers for this culture has diminished greatly due to the lack of demand for linseed oil on the Romanian market. In the paper we have conducted an economic efficiency study for this crop, for two years, a study conducted within a plant farm in Timis County.

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)0.004
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.249
Teacher spread0.239 · 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
Published2019
Admission routes1
Has abstractyes

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Same venueLucrări Științifice, Universitatea de Științe Agricole Și Medicină Veterinară a Banatului, Timisoara, Seria I, Management AgricolSame topicVaried Academic Research TopicsFrench-language works237,207