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Record W4413258829 · doi:10.5376/mgg.2024.15.0030

Advances in Standardized Cultivation Systems for Fresh-Eating Maize

2024· article· en· W4413258829 on OpenAlexvenueno aff
Haibo Wang

Bibliographic record

VenueMaize Genomics and Genetics · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyBiologyBiotechnologyAgricultural engineeringEngineering

Abstract

fetched live from OpenAlex

Fresh-eating maize, as an important crop with both economic and nutritional value, holds a significant position in modern agriculture. This study systematically explores the key elements of the standardized cultivation system for fresh-eating maize, including ecological and environmental requirements, variety selection and adaptability, core cultivation techniques, and the construction and promotion of the system. In terms of ecological and environmental requirements, the study clarifies the basic conditions for fresh-eating maize growth, the impact of regional cultivation management, and refines the ecological needs and management priorities at different growth stages. For variety selection, the study analyzes the characteristics of common high-quality varieties and their regional adaptability screening methods, while proposing standardized selection criteria. Core cultivation techniques cover areas such as soil management, planting density, water and fertilizer regulation, and field growth control. Through case studies, the research evaluates the significant role of the standardized cultivation system in improving the yield and quality of fresh-eating maize and reveals the contributions of technology promotion to agricultural sustainability and industry scaling. The study further envisions the integration of green agriculture and intelligent technologies, sustainable development pathways, and future research priorities. This research provides scientific evidence and practical guidance to promote efficient and high-quality production of fresh-eating maize.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.245
Teacher spread0.228 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMaize Genomics and GeneticsSame topicCrop Yield and Soil FertilityFrench-language works237,207