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Record W2963599529 · doi:10.5539/jas.v11n13p42

A Preliminary Evaluation of Lablab Biomass Productivity in Virginia

2019· article· en· W2963599529 on OpenAlexvenueno aff
Harbans L. Bhardwaj, Anwar A. Hamama

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsLablab purpureusForageBiomass (ecology)Dry weightProductivitySowingAgronomyCropAnimal scienceCrop residueChemistryBiologyLegumeEcologyAgriculture

Abstract

fetched live from OpenAlex

A field study was conducted for two years with seventeen lablab [Lablab purpureus (L.) Sweet] lines to characterize its productivity under Virginia’s agro-climatic conditions and to determine lablab’s potential as a forage crop. One sample per replication (0.3 m row length) was harvested approximately 90 days after planting to record fresh weight. These samples were dried to a constant weight to record dry weights. Dry and fresh yields were not affected by lines and year of production. Overall means of fresh and dry yields varied from 47 to 91 with a mean of 62, and 9 to 15 with a mean of 13 Mg/ha, respectively. Year of production had significant effects on concentrations of P, K, S, Mg, Mn, Cu, and Zn. Concentrations of protein, P, K, Ca, Mg, S, Al, B, Cu, Fe, Mn, Na, and Zn in lablab produced in Virginia were 15, 0.28, 2.30, 1.32, 0.27, 0.22, 224, 20, 18, 343, 79, 0.03, and 40, respectively. Quality of lablab forage compared well with literature values of other forage legumes especially alfalfa. Lablab biomass in this study contained 60, 45, and 15 percent ADF, NDF, and lignin, respectively indicating that it may also be a potential feedstock for bio-ethanol manufacture.

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)0.003
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.175

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.253
Teacher spread0.230 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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