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Record W1977889340 · doi:10.2134/agronj14.0397

Timothy Response to Increasing Rates of Selenium Fertilizer in Eastern Canada

2014· article· en· W1977889340 on OpenAlexafffundabout
Gaëtan F. Tremblay, Gilles Bélanger, Julie Lajeunesse, P.Y. Chouinard, Édith Charbonneau

Bibliographic record

VenueAgronomy Journal · 2014
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Council CanadaDairy Farmers of Canada
KeywordsSeleniumFertilizerAnimal scienceForageDry matterChemistryAgronomyHuman fertilizationBiology

Abstract

fetched live from OpenAlex

Selenium is an essential micronutrient given to ruminants by injection or orally or applied as an additive to fertilizers to raise crop Se concentrations in areas with low‐Se soils. We determined the response of timothy ( Phleum pratense L.) Se concentration to increasing rates (0, 5, 10, 15, 20, and 25 g Se ha −1 ) of a slow‐release Se fertilizer (Selcote Ultra) applied in spring of 2010 at three sites in Québec, Canada, and investigated the possibility of predicting forage Se concentration by near‐infrared reflectance spectroscopy (NIRS). The response of timothy Se concentration ( y , mg kg −1 dry matter, DM) to increasing Se rates ( x ) was similar at the three sites; averaged across sites, it increased linearly with increasing Se rates at the first ( y = 0.012 + 0.0292 x ; R 2 = 0.98) and second harvests ( y = 0.052 + 0.0091 x ; R 2 = 0.96) in the year of spring Se application and in the first harvest of the subsequent year ( y = 0.012 + 0.0117 x ; R 2 = 0.97). Selenium fertilization did not affect timothy DM yield, fiber concentration, or digestibility. Timothy Se concentration could not be successfully predicted by NIRS. A spring application of 10 g Se ha −1 as a slow‐release fertilizer and its residual effect are sufficient to produce timothy with an adequate Se concentration (>0.1 mg kg −1 DM) to prevent deficiency diseases in livestock and allow diet formulation to meet optimal Se levels.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.081
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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

Citations12
Published2014
Admission routes3
Has abstractyes

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