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Record W6922029204 · doi:10.1139/cjps10153

Condensed tannin concentrations found in vegetative and mature forage legumes grown in western Canada

2011· article· en· W6922029204 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsLotus corniculatusCondensed tanninForageLegumeTanninRuminantFabaceaeMedicago sativa

Abstract

fetched live from OpenAlex

Berard, N. C., Wang, Y., Wittenberg, K. M., Krause, D. O., Coulman, B. E., McAllister, T. A. and Ominski, K. H. 2011. Condensed tannin concentrations found in vegetative and mature forage legumes grown in western Canada. Can. J. Plant Sci. 91: 669-675. There has been limited effort to examine condensed tannin (CT) concentrations of forage legume species grown in western Canada. Using the butanol-HCl technique, extractable CT concentrations were measured in Trifolium hybridum L., T. ambiguum M. Bieb, T. pratense L., T. repens L., Dalea purpurea Vent., Onobrychis viciifolia Scop., Lotus corniculatus L., Medicago sativa L., and Astragalus cicer L. collected from research and variety trial plots across the Canadian prairies. Above ground plant biomass was harvested at the vegetative and mature physiological stages for two growing seasons. Dalea purpurea, a native legume, had the highest mean CT concentration of 68.6±22.6 g kg-1 DM, with minimum and maximum values ranging from 37.9 to 92.9 g kg-1 DM. Onobrychis viciifolia had the second highest mean CT concentration (46.0 g kg-1 DM) with a range of 16.3 to 94.4 g kg-1 DM. The third highest mean CT concentration of 15.1 g kg-1 DM was found in L. corniculatus with a range of 0.0 to 25.7 g kg-1 DM. Forage biomass had higher CT concentrations (P<0.05) when harvested at the mature stage for all species except O. viciifolia, which had higher CT concentrations in the vegetative state. The potential benefit of inclusion of these species in ruminant diets at the observed concentrations requires further exploration.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.402
GPT teacher head0.318
Teacher spread0.085 · 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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