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Record W2009063797 · doi:10.5539/jas.v2n2p164

Wheat Production in India: Technologies to Face Future Challenges

2010· article· en· W2009063797 on OpenAlexvenueno aff
Rajbir Yadav, S S Singh, Neelu Jain, Gyanendra Pratap Singh, K. V. Prabhu

Bibliographic record

VenueJournal of Agricultural Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsGreen RevolutionAgricultureProduction (economics)Investment (military)Selection (genetic algorithm)Scale (ratio)Task (project management)Natural resource economicsYield (engineering)BusinessNatural resourceEmerging technologiesFace (sociological concept)AgroforestryBiotechnologyAgricultural economicsGeographyEngineeringComputer scienceEconomicsBiologyEcologyPolitical science

Abstract

fetched live from OpenAlex

To meet the growing demands under the constrains of depleting natural resources, environmental fluctuation andincreased risk of epidemic outbreak, the task of increasing wheat production has become daunting. The euphoriagenerated by first green revolution is very quickly subsiding and the second generation problems are becomingmore intense with each passing year. The factors responsible for first green revolution seem to be exhaustingrapidly and there is immediate need to develop the technologies which can not only increase the wheatproduction but also sustain at higher level without adversely affecting the natural resources. More investment ongermplasm improvement, conservation agriculture including breeding for varieties adaptive to conservationagriculture, hybrid wheats, broadening the genetic base of the varieties at farmers level, wide scale utilization ofalien translocations in the breeding programme along with integration of marker assisted selection and otherinnovative approaches with traditional breeding methods are some of the technologies which can yield dividendin the coming years.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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 designBench or experimental
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

Citations41
Published2010
Admission routes1
Has abstractyes

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