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Record W2972864432 · doi:10.5539/jas.v11n16p213

Initial Growth and Roots Development of Soybean as Function of Water Availability and Soil Bulk Density

2019· article· en· W2972864432 on OpenAlexvenueno aff
Pâmela de Andrades Timm, Marília Alves Brito Pinto, José Maria Barbat Parfitt, Germani Concenço, Alexssandra Dayanne Soares de Campos, Thayse do Amaral Aires, Jaqueline Trombetta da Silva, Lessandro Coll Faria

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsHydric soilSoil compactionBulk densitySoil waterAgronomyContext (archaeology)CropEnvironmental scienceGreenhouseField capacitySoil structureWater potentialCompactionBiologySoil scienceMaterials science

Abstract

fetched live from OpenAlex

Soil compaction is preponderant in soil physical-hydric relationships, which in turn, exert direct effect on plant development. In this context, this work aimed to evaluate the initial development of shoot and roots of soybean plants (Glycine max (L.) Merril), cv. BMX Ícone, cultivated in different combinations of soil bulk densities and water availability. A greenhouse experiment was carried out at the EMBRAPA Lowland Experimental Station, Rio Grande do Sul, Brazil. Soybean plants were grown in seven levels of soil bulk density (1.4, 1.5, 1.6, 1.7, 1.8, 1.9 and 2.0 kg dm-3) coupled to two soil water tensions (10 and 50 kPa). Plant height and leaf area, as well as root volume, decreased when soybean was cultivated at 50 kPa, associated to soil bulk densities above 1.8 kg dm-3. Soybean crop showed to be most sensitive to water deficit than to soil compaction, and soil water tension around the field capacity (10 kPa) should be associated to soil bulk density lower than 1.8 kg dm-3 to allow adequate soybean crop development.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 integrity0.0000.000
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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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 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
Published2019
Admission routes1
Has abstractyes

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