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Record W2348458209

The Vertical Distribution Pattern of Alfalfa's(Medicago sativa L.) Root Biomass

2012· article· en· W2348458209 on OpenAlexaboutno aff
Yang Li

Bibliographic record

VenueActa Agrestia Sinica · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsTopsoilAgronomyBiomass (ecology)Medicago sativaIrrigationSowingEnvironmental scienceSoil horizonSoil waterBiologySoil science
DOInot available

Abstract

fetched live from OpenAlex

In order to provide the evidences for further research of alfalfa(Medicago sativa L.),the substantial studies of alfalfa root biomass in China,the United States and Canada were summarized.The vertical distribution pattern of alfalfa root biomass and its dominant factors in different natural regions were discussed.Factors included the depth of soil layer,underground water level,soil properties,fertilization,irrigation,cutting,mixture sowing,cultivar,and growth period.The distribution of alfalfa root biomass in topsoil was increased as the depth of soil layer lessened and the level of underground water increased,or soil conditions became worse.Fertilization,especially phosphate fertilizer increased the distribution in submerged soil.Different irrigation amount,frequency and pattern had the complex effect on vertical distribution of alfalfa root biomass.Less single irrigation amounts and higher irrigation frequency increased the distribution of alfalfa root biomass in topsoil.Higher cutting frequency decreased the distribution in submerged soil.Mixture sowing with graminous plants increased the distribution of alfalfa root biomass in topsoil.Alfalfa cultivars have different vertical distribution patterns of root biomass.The soil layer of vertical distribution became deeper as the plants grew.Alfalfa root biomass decreased exponentially with soil depth increasing.Under normal conditions,the distribution ratios of alfalfa root biomass in the 0~30 cm depth were about 60% to 90%,and in the 0~60 cm depth were about 65% to 95%.

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 score0.108
Threshold uncertainty score0.768

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.210 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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