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Record W2319742893 · doi:10.1021/jf403245c

Characterizations of Structural, Biochemical, and Nutritive Profiles in Silage among Cool-Season Corn Cultivars in Relation to Heat Units (aCHU, dCHU) with Curvilinear Response and Multivariate Analyses

2013· article· en· W2319742893 on OpenAlexaffabout
Saman Abeysekara, David A. Christensen, Peiqiang Yu

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Soil, Plant Science
Canadian institutionsUniversity of Saskatchewan
FundersNational Science Council
KeywordsSilageCultivarMultivariate statisticsAgronomyCurvilinear coordinatesBiologyChemistryMathematics

Abstract

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Molecular spectroscopy is able to reveal structural features of biomaterials. Corn grown in Canadian prairies is known as cool-season corn, which is different from warm-season corn varieties. To our knowledge, to date, there has been no study on the magnitude difference in structure on a molecular basis among cultivars, no study on biochemical and nutritive profiles associated with heat unit, and no study on how heat unit affects the molecular structure and biochemical and nutritive profiles. This study investigates how corn varieties grown in cooler climates are affected by crop heat units (CHU) in relation to molecular spectral profiles, nutrient storage, biochemical composition, and nutritive value of silage among different cool-season corn cultivars. Corn cultivars (Pioneer and Dekalb) were from seven farm locations, and samples were analyzed for major nutrients (digestible and metabolic energy and protein). The Fourier transform infrared (FT/IR) spectroscopic technique was applied to understand and differentiate molecular structural spectral profiles in silage. A correlation (P < 0.05) of CHU with some nutrients (mean ± SD, %DM) (CP, 8.1 ± 1.3, r = 0.56; NDF, 56.3 ± 3.5, r = -0.54; ADF, 33.6 ± 2.3, r = -0.71; NDICP, 1.6 ± 0.4, r = -0.66; SCP, 4.2 ± 1.3, r = 0.61), protein and carbohydrate fractions (mean ± SD, %DM) (PB1 (= fast degradable protein fraction), 1.3 ± 0.4, r = 0.54; PB3 (= slowly degradable protein fraction), 1.5 ± 0.4, r = -0.74; CB2 (= medium degradable carbohydrate fraction), 45.1 ± 2.8, r = -0.65; CB3 (= slowly degradable carbohydrate fraction), 13.9 ± 0.9, r = -0.54) and intestinal availability of ruminally degraded fractions (mean ± SD, %DM) (rdPB1, 1.1 ± 0.3, r = 0.54; rdPB3, 1.0 ± 0.3, r = -0.74; RDP, 6.6 ± 1.2, r = 0.59; rdCB2, 40.0 ± 2.5, r = -0.65; rdCB3, 8.9 ± 0.6, r = 0.54; RDCHO, 50.1 ± 2.9, r = -0.65) was found contentious. Molecular spectral data indicated many similarities and few differences among the cultivars. However, CHU correlated (r = -0.4, P < 0.05) with molecular spectral intensity ratio of carbohydrate to amide I. This result indicates that molecular structural differences may be influenced by epiphytic bacterial compounds. Cool corn cultivars were grown acceptably well in cooler dry climates, and those silages had acceptable nutrient levels for cattle. Cultivars that reached target CHU were found to be optimal in nutrient and energy synchronization aspect.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.228
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 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

Citations14
Published2013
Admission routes2
Has abstractyes

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