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Record W2579974885 · doi:10.5539/sar.v6n2p13

Field Testing of Efficacy of Three Environmentally Friendly Insecticides Against Colorado Potato Beetle (Leptinotarsa Decemlineata [Say], Coleoptera, Chrysomelidae) on Potato-Evaluation of the Effect on Yield

2017· article· en· W2579974885 on OpenAlexvenueno aff
Žiga Laznik, Stanislav Trdan

Bibliographic record

VenueSustainable Agriculture Research · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsnot available
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsLeptinotarsaColorado potato beetleAzadirachtinYield (engineering)HorticultureAgronomySolanumBiologyChemistryPEST analysisPesticide

Abstract

fetched live from OpenAlex

In 2007 and 2008 the field experiment was conducted to test the efficacy of three environmentally friendly insecticides against the Colorado potato beetle (Leptinotarsa decemlineata), with the aim of evaluating their effect on the yield of potato. 0.25 % water emulsion of Neem-Azal (active ingredient azadirachtin) was applied twice, while 3 % water emulsion of Aktiv (a.i. potassium salt of fatty acids) and 1 % water emulsion of Prima (a.i. refined rape oil) were applied eight times. In 2007, the potato yield was higher (25.3±3.2 t ha-1) than in 2008 (8.2 ± 0.8 t ha-1). In 2007 there were no significant differences in potato yield at different control measures and the yield ranged from 7.5 ± 1.3 t ha-1 (Aktiv) to 9.4 ± 1.8 t ha-1 (Prima). In 2008, the highest potato yield was recorded in Neem-Azal treatment (3.5 ± 0.7 t ha-1), while in two other insecticide treatments the potato yield did not differ significantly with control treatment neither with the Neem-Azal treatment. Potato tubers were classified into three fractions: fraction 1 (tubers <4 cm), fraction 2 (tubers between 4 and 5 cm), and fraction 3 (tubers > 5 cm). On average we produce 2.11 ± 0.06 t ha-1, 9.93 ± 0.53 t ha-1, and 13.17 ± 0.70 t ha-1 of potato in 2007, and 2.11 ± 0.20 t ha-1, 4.68 ± 0.37 t ha-1, 0.84 ± 0.29 t ha-1 of potato in 2008, respectivelly.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.300
Teacher spread0.252 · 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.

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

Citations2
Published2017
Admission routes1
Has abstractyes

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