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Record W3162301556 · doi:10.5539/jms.v11n1p168

A Research on the Fruitfulness of the Reddish-Yellow Acrisol in Serra da Meruoca, Ceará, Brazil

2021· article· en· W3162301556 on OpenAlexvenueno aff
Marcos Venicios Ribeiro Mendes, Simone Ferreira Diniz, Cleire Lima da Costa Falcão, José Falcão Sobrinho, Francisca Edineide Lima Barbosa

Bibliographic record

VenueJournal of Management and Sustainability · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsArable landVegetation (pathology)AgricultureLeaching (pedology)Environmental scienceHydrology (agriculture)Soil scienceGeographySoil waterGeologyArchaeology

Abstract

fetched live from OpenAlex

Agriculture, to be successful, needs soil to have a potential nutrient composition that is relevant to plants. Therefore, it is necessary to identify the conditions for farming through soil analysis. Thus, this manuscript makes it possible to analyze the fruitfulness of two samples of the reddish-yellow acrisol—one with vegetation and the other without it—from Serra da Meruoca, a humid area in the semi-arid region of Ceará. Concerning the material and method, the stages were literature review, researches on cartographic bases, fieldwork, and data collection and their analysis in the laboratory. The results show that the area of acrisol with vegetation favors the practice of agriculture, a fact observed because of carbon (C), which is indicative of soil with intense cultivation, as well as calcium (Ca), which appeared in a significant level, typical of arable land. In the second sample, the acrisol without vegetation, the parameters that impose restrictions on agriculture are the pH, which contains exchangeable aluminum, indicative of high acidity that leads to a leaching process. Also, the aluminum (Al) at a low level reflected the need for dolomitic quicklime, for the amendment of a deficient soil. Therefore, studies on its fruitfulness are essential for farmers to reap the rewards according to the results obtained and analyzed.

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)0.003
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 score0.300
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.041
GPT teacher head0.309
Teacher spread0.268 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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