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Record W3139221633 · doi:10.21608/ejar.2014.156773

SIDE EFFECTS OF USING OF SOME FOLIAR FERTILIZERS ONBIOLOGY OF COTTON LEAF WORM, SPODOPTERA LITTORALIS (BOISD.) UNDER LABORATORY CONDITIONS

2014· article· en· W3139221633 on OpenAlexaboutno aff
ABDEL-MASEH W. MAKKAR, E. Mansour, Aml Abd-Allah

Bibliographic record

VenueEgyptian Journal of Agricultural Research · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsSpodoptera littoralisBiologyBotanyHorticultureSpodopteraAgronomyNoctuidaeLarva

Abstract

fetched live from OpenAlex

An experiment was conducted at Qaha Research Station, Qalubia governorate, during 2013 cotton season, to study the effects of four foliar fertilizers on biological aspects of cotton leaf worm, Spodoptera littoralis (Boisd.), for three successive generations. (Canada magic, Canada sal, Canada foliar and Canada amino) were sprayed on cotton leaves in field and introduced to the newly hatched larvae. The obtained results revealed that the larval stages suffered greatest mortality followed by the pre-pupal stage, the pupal stage and lastly the moths for the four compounds. The highest mean percentage mortality of overall immature stages within the three generations (98.8, 98.2, 98.0 and 96.3%) was (Canada sal, Canada amino, Canada magic and Canada foliar), respectively. Analysis of variance between the mean larval duration and Percentage pupation for the remaining larvae and pupae of the three generations, showed significant differences between treatments and control (L.S.D =2.33 & 6.3), respectively. Moreover, all larvae fed on treated leaves gave the least percentage of adults, emergence and a few malformations, also, no eggs were laid by the resulting females in the three generations. Data demonstrated that the larval and pupal weights recorded insignificant differences between treated and untreated.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.911
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.048
GPT teacher head0.316
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 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
Published2014
Admission routes1
Has abstractyes

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