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Record W3041880044 · doi:10.46534/jliv.2019.06.01.044

Assessment of natural enemies for pest control on an indoor living wall

2019· article· en· W3041880044 on OpenAlexafffund
S. Bjørnson, Jay Gallant

Bibliographic record

VenueJournal of Living Architecture · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsNova Scotia HospitalSaint Mary's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPEST analysisBiological pest controlMealybugBiologyBiological dispersalPest controlEcologyPredatorPredationChrysoperla carneaHorticultureChrysopidaePopulation

Abstract

fetched live from OpenAlex

Living walls are comprised of tropical plants that are susceptible to a variety of insect pests, but little information is available to help achieve effective pest control on vertical plant canopies. Although insect natural enemies can provide efficient pest control in indoor environments, the vertical canopies of living walls present challenges for natural enemy dispersal and success. During this six-month study, commercially-available natural enemies were released to control aphid outbreaks and a heavy infestation of soft brown scales on an indoor living wall. Three lady beetles were used: Lindorus (Rhyzobius) lophanthae (scale destroyer) and Cryptolaemus montrouzieri (mealybug destroyer) were released for scale control, and Adalia bipunctata (two-spotted lady beetle) was released to control aphids. Chrysoperla carnea (green lacewings) were released to observe natural enemy migration. Brown soft scale on Schefflera was reduced from 83.2 to 7.5 scales per leaflet (mid-May to mid-October; 91% reduction). C. montrouzieri was the only natural enemy able to establish itself on the wall, and their larvae were easy to monitor. Although the physical environment of the upper and lower canopies differed considerably, the natural enemies used were able to migrate freely and provide effective pest control.

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)0.000
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 score0.902
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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