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Record W4297987027

Determining a critical nitrogen dilution curve in Miscanthus x giganteus

2015· preprint· en· W4297987027 on OpenAlexaff
Marion Zapater, M. Ollier, Bruno Mary, Manuella Catterou, Fabien Ferchaud, Catherine Giauffret, Loïc Strullu, Stéphanie Arnoult-Carrier, Frank Dubois, Maryse Brancourt‐Hulmel

Bibliographic record

VenueProdinra (INRA Bordeaux-Aquitaine) · 2015
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsImpact
FundersAgence Nationale de la Recherche
KeywordsMiscanthusNitrogenDilutionEnvironmental scienceComputer scienceChemistryBiologyThermodynamicsPhysicsBiotechnologyBioenergyOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Miscanthus x giganteus is a promising bioenergy crop, combining high biomass production and low nutrients requirements, thanks to an efficient nutrient recycling (Strullu et al, 2011;Cadoux et al, 2013).The important fluxes of nitrogen (N) recycling from the rhizome makes the evaluation of the crop N status difficult.Reliable indicators of crop N status are needed to improve detection of N deficiency and define N fertilization strategies for sustainable crop production.Among the available indicators, the critical N dilution curve is a fruitful concept (Lemaire and Gastal, 1997).Critical N is defined as the minimum concentration of N required in shoots at a given time to maximize the aboveground biomass.The aim of this work was to determine the critical N dilution curve for Miscanthus x giganteus in a dedicated experiment and validate it using published data.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.284
Teacher spread0.238 · 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 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

Citations0
Published2015
Admission routes1
Has abstractno

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