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Record W7027066477

Celebrating the golden jubilee of Ratarstvo i povrtarstvo and the diamond jubilee of the Institute of Field and Vegetable Crops

2013· article· en· W7027066477 on OpenAlexaboutno aff

Bibliographic record

VenueFiVeR (Institute of Field and Vegetable Crops, Novi Sad, Serbia) · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsLegumeEditorial boardFoundation (evidence)
DOInot available

Abstract

fetched live from OpenAlex

The newly founded International Legume Society early this year is making its first steps towards the first Legume Society Conference, an event which aim is to connect legume researchers worldwide and establish well-linked legume community. Its host this year, the Institute of Field and Vegetable Crops (IFVCNS) is on its well-trodden path and celebrating its diamond jubilee, 75th years since its foundation in 1938. Another less known, but worth remembering anniversary is the golden jubilee of a journal Ratarstvo i povrtarstvo (Field and Vegetable Crops) published by the IFVCNS. Starting as Zbornik radova (Review of Research Work) in 1963, in the course of 50 years, the journal published 1651 papers in total, 402 of which are devoted to legume crops. The frequency of the research papers on legumes has steadily increased from 11.7% in the first decade, up to 27.3% till today. The most numerous papers were devoted to lucerne (10.4%), soybean (9%), vetches (7.5%), clovers (6.1%) and pea (5.8%). In total 144 legume species have been investigated in the journal. Out of 55 papers with at least one author out of Serbia and other ex-Yugoslavian countries, 45 papers were published in the last decade. The editorial board comprises 56 editors, a half of them are from Bulgaria, Canada, China, Czech Republic, Finland, France, Germany, Hungary, India, Iraq, Ireland, Romania, Russia, Spain, Turkey, UK and USA.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.404
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.182
Teacher spread0.174 · 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 teacher head, 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
Published2013
Admission routes1
Has abstractyes

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