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Record W1952674214 · doi:10.7202/1031955ar

Relative susceptibility of the Bikaner and Delhi populations of mustard aphid, Lipaphis erysimi (Kalt.) (Homoptera: Aphididae), and its predator, Coccinella septempunctata L. (Coleoptera: Coccinellidae), to different insecticides

2015· article· en· W1952674214 on OpenAlexvenueno aff
K. Shankarganesh, Sachin S. Suroshe, Bishwajeet Paul

Bibliographic record

VenuePhytoprotection · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsThiamethoxamAcetamipridLipaphis erysimiCoccinella septempunctataImidaclopridBiologyCarbosulfanCoccinellidaeBifenthrinToxicologyAphidPopulationHorticulturePredatorAgronomyPesticidePredationMedicineEcology

Abstract

fetched live from OpenAlex

A study was undertaken to assess the effectiveness of five insecticides against the Delhi and Bikaner populations of mustard aphid, Lipaphis erysimi (Kalt.), using the leaf dip method, and against Coccinella septempunctata L. in semi-field conditions. Acetamiprid and thiamethoxam were found to be more toxic than other insecticides. After 24 h, the LC 50 values for the Bikaner population against different insecticides were 7.0, 6.0, 4.0, 3.0 and 2.0 ppm for carbosulfan, bifenthrin, imidacloprid, acetamiprid and thiamethoxam, respectively. Similarly, the descending order of toxicity for the Delhi population was acetamiprid (7.0 ppm), thiamethoxam (9.0 ppm), imidacloprid (15.0 ppm), carbosulfan (32.0 ppm) and bifenthrin (36.0 ppm). The relative toxicity values suggest that in both populations, thiamethoxam and acetamiprid show the highest toxicity. Carbosulfan and bifenthrin were highly toxic to coccinellid grubs and resulted in 100% mortality in semi-field conditions, whereas the neonicotinoids acetamiprid and thiamethoxam showed less mortality. It showed the tolerance of coccinellidae against neonicotinoids under semi-field conditions.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.242
Teacher spread0.202 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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