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Record W2987895167 · doi:10.5539/jas.v11n18p166

Neosilba Perezi (Romero & Ruppel, 1973) (Diptera: Lonchaeidae) Damage Simulation on the Production of Stem Cuttings and the Productive Aspects of Cassava

2019· article· en· W2987895167 on OpenAlexvenueno aff
Humberto Godoy Androcioli, Adriano Thibes Hoshino, Juliana Sawada Buratto, João Henrique Caviglione, Rodolfo Bianco, Luciano Mendes de Oliveira, Adevanir Martins dos Santos, Marcelo Augusto Pastório, Wilmar Ferreira Lima

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsCuttingMeristemProductivityBiologyShootHorticultureBotanyAgronomy

Abstract

fetched live from OpenAlex

The loss caused by the shoot-fly (Neosilba perezi) was simulated through the elimination of the cassava`s terminal buds, with the objective to quantify this effect towards the roots productivity, flour production and propagative material production (stem cuts). The experimental design was random blocks, with three repetitions, in a factorial scheme with an additional treatment (10 × 4 + 1). Ten damage levels (10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%) were studied, including a witness treatment (0%). The cassavas were treated during each of the four seasons of the year. The studied variables were: root productivity (t ha-1), flour production (t ha-1) and number of stem cuttings with a diameter greater than 20 and 25 mm. The induced damage in the cassava`s meristem did not significantly affect the root, nor the flour production. However, the stem cuttings production demonstrated a linear decrease in relation to the apical meristem damage level.

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: Simulation or modeling · 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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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 designSimulation or modeling
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
Published2019
Admission routes1
Has abstractyes

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