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Record W4403949788 · doi:10.5376/mpb.2024.15.0026

Diversity and Cultivation of Sugarcane: From Traditional Practices to Modern Breeding Techniques

2024· article· en· W4403949788 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMolecular Plant Breeding · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSugarcane Cultivation and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyDiversity (politics)BiotechnologyGenetic diversityPlant breedingAnimal breedingAgroforestryAgronomy

Abstract

fetched live from OpenAlex

Genome selection, marker-assisted breeding and the integration of biotechnology methods are accelerating the development of excellent sugarcane varieties. This study explores the diversity and cultivation of sugarcane with a focus on the evolution from traditional practices to modern breeding techniques, aiming to study the genetic diversity of sugarcane species, assess traditional and modern cultivation methods, and highlight advances in breeding techniques that significantly increase yield, disease resistance, and environmental adaptability. The findings show that while traditional methods provided the foundation for sugarcane cultivation, modern genomic tools and molecular breeding methods have revolutionized crop improvement, increasing productivity and sustainability, and the combination of genetic diversity with advanced breeding techniques is expected to further optimize sugarcane cultivation, contributing to global agriculture and biofuel production.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.317

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 head0.093
GPT teacher head0.254
Teacher spread0.161 · 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