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Record W2149712404 · doi:10.1614/wt-d-14-00112.1

Nutritive Value of Field Bindweed (<i>Convolvulus arvensis</i>) Roots as a Potential Livestock Feed and the Effect of <i>Aceria malherbae</i> on Root Components

2015· article· en· W2149712404 on OpenAlexaboutno aff
Brian J. Schutte, Leonard M. Lauriault

Bibliographic record

VenueWeed Technology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsConvolvulusAgronomyPerennial plantBiologyWeedLivestockForageNutrientMentha arvensisBotanyEcology

Abstract

fetched live from OpenAlex

Crop producers might be able to better manage field bindweed, an aggressive perennial weed, by utilizing tillage to bring roots to the surface where they can be consumed by ruminant livestock. The objectives of this study were to provide first perspectives on forage nutritive value of field bindweed roots and to determine root chemistry responses to Aceria malherbae , an eriophyid mite that has been released for field bindweed biocontrol in the western United States and Canada. To accomplish these objectives, root systems were sampled from A. malherbae -infested and noninfested plants occurring in an agricultural field in eastern New Mexico. Sampling took place during autumn and spring of each year for 3 consecutive yr. Results indicated that A. malherbae reduced taproot diameter and increased root concentrations of Ca, P, and Mg. However, A. malherbae did not affect root concentrations of acid detergent fiber, nonfiber carbohydrates, neutral detergent fiber (NDF), crude protein (CP), and total digestible nutrients (TDN). Overall means for NDF (33.8%), CP (11.6%), and TDN (72.1%) were similar to those reported for forages commonly grown in the region, suggesting that field bindweed roots might positively contribute to nutritional programs of ruminant livestock. These results justify subsequent studies on livestock responses to field bindweed roots and field bindweed responses to targeted root grazing.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.305

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 designBench or experimental
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

Citations6
Published2015
Admission routes1
Has abstractyes

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