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Record W4403172541 · doi:10.1201/9781003578499-2

Improving Maize Grain Yield Potential in a Cool Environment

2024· book-chapter· en· W4403172541 on OpenAlexaboutno aff
M. Tollenaar, Wu Jiangang

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsYield (engineering)Grain yieldAgronomyAgricultural engineeringEnvironmental scienceZea maysAgroforestryMaterials scienceBiologyEngineeringMetallurgy

Abstract

fetched live from OpenAlex

A retrospective analysis of the physiological basis of yield improvement may provide an understanding of yield potential and indicate avenues for future yield improvement. Average maize grain yield per unit area in Ontario has increased at a rate of approximately 1.5 percent per year during the five-decade period since the introduction of hybrids in the 1940s, but average yields changed little during the five-decade period prior to hybrid introduction (see Figure 2.1 ). There is little doubt that hybrid vigor, which resulted in increased grain yield, decreased lodging, and increased stand uniformity of hybrids over open-pollinated varieties, contributed to the onset of the substantial and consistent yield improvements in maize. An average yield increase of 15 percent is commonly attributed to heterosis ( Frey, 1971 ). It is not clear, however, whether heterosis per se has contributed beyond the initial increase.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.179
Teacher spread0.163 · 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
Published2024
Admission routes1
Has abstractyes

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