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Record W2796585773 · doi:10.5539/jas.v10n5p34

Specific Management Areas as a Function of Dendrometric Properties of Eucalyptus and Physical-Chemical Attributes in an Oxisol

2018· article· en· W2796585773 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsOxisolEucalyptus camaldulensisEucalyptusSampling (signal processing)Soil scienceEnvironmental scienceSoil textureSoil fertilityMathematicsForestrySoil waterGeographyEngineeringBotany

Abstract

fetched live from OpenAlex

Eucalyptus cultivation has expanded considerably in Brazil, especially in regions where soils have low fertility, as in the Brazilian Cerrado (Brazilian Savannah). In order to achieve high yield, it is necessary to know the appropriate time and place to perform the soil management, and to assist in this decision-making process, mathematical and computational models has been used and are a promising alternative. The objective of this study was to model the influence of plant and soil physical-chemical attributes on Eucalyptus camaldulensis cultivation in an Oxisol (Latossolo Vermelho distrófico), with clayey texture with the purpose of demonstrating specific management areas closely associated with eucalyptus development. An experimental grid of approximately 2 hectares (ha) containing 40 sampling points were installed and later soil and plant attributes were collected for the determination of physical and chemical attributes in the 0-0.20 m and 0.20-0.40 m layers in Selvíria, MS, Brazil. The results were analyzed using classical and geostatistical statistics. The spatial dependence varied according to the physical attribute evaluated and the depth of sampling. In addition to the vertical variability, there was also horizontal variability between depths, since for the same attribute the range was different between the sampled layers.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.220
Teacher spread0.190 · 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