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Record W7120329109

MATÉRIA ORGÂNICA COMO ATENUANTE DA SALINIDADE DA ÁGUA DE IRRIGAÇÃO NA CULTURA DO MILHO

2020· article· pt· W7120329109 on OpenAlexvenueno aff
Rilbson Henrique Silva dos Santos, Mirandy Dos Santos. DIAS, Francisco de Assis Tavares Ferreira da Silva, João Paulo de Oliveira Santos, Saniel Carlos dos Santos, Lígia Sampaio REIS, Clécio Lima Tavares

Bibliographic record

VenueCanadian acoustics · 2020
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicGrowth and nutrition in plants
Canadian institutionsnot available
Fundersnot available
KeywordsSalinityDry matterShootSoil salinityOrganic matterCompletely randomized designChlorophyllCrop
DOInot available

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the use of organic matter in the development of corn irrigated with saline water. The experiment was carried out at the Agrarian Sciences Center of the Federal University of Alagoas. The experimental design was completely randomized in a 5 x 2 factorial scheme, with four replications, being the treatments: 5 levels of salinity (0.5 (control); 1.5; 3.0; 4.5 and 6.0 dS M-1) with presence and absence of organic matter. Saline solutions were prepared with NaCl and distilled water, and the formula: TSD (G L-1) = 0.64 x CEa was used. At 40 days after emergence, the plants were collected and analyzed for the number of leaves, leaf area, plant height, relative leaf chlorophyll index, shoot dry mass and root system dry mass. It was Found that all the variables studied in the maize crop were affected by the increase in salinity levels and that the soil decreased the effects of salinity when organic matter was added.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.051
GPT teacher head0.238
Teacher spread0.187 · 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
Published2020
Admission routes1
Has abstractyes

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