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Record W4393551384 · doi:10.5281/zenodo.8025215

Supplementary Tables for Can leafhoppers help us trace the impact of climate change on agriculture?

2023· supplementary-materials· en· W4393551384 on OpenAlexaffabout
Nicolas Plante, Jeanne Durivage, Anne‐Sophie Brochu, Tim Dumonceaux, Dagoberto Torres, Brian W. Bahder, Joel H. Kits, Antoine Dionne, Jean‐Philippe Légaré, Stéphanie Tellier, Frédéric Mcune, Charles Goulet, Valérie Fournier, Edel Pérez‐López

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typesupplementary-materials
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsAgriculture and Agri-Food CanadaMinistère de l'Agriculture, des Pêcheries et de l'AlimentationUniversité Laval
Fundersnot available
KeywordsTRACE (psycholinguistics)AgricultureClimate changeEnvironmental scienceGeographyAgroforestryAgricultural economicsPhysical geographyAgricultural engineeringEconomicsEcologyArchaeologyBiologyEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Supplementary Tables for the Preprint entitled: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv. <strong>Table S1. </strong>Detailed information on the strawberry fields included in this study. <strong>Table S2</strong>. Detailed information on the weather stations used to retrieve temperature and precipitation data used in this study <strong>Table S3. </strong>Strawberry samples analyzed in this study with symptoms resembling strawberry green petal phytoplasma disease during both growing seasons studied here. <strong>Table S4.</strong> The geographic location of all the strawberry green petal phytoplasma disease cases reported to the provincial laboratory in expertise in diagnostic and phytopathology in the last decade. <strong>Table S5.</strong> Leafhopper species and the number of specimens per species analyzed by phytoplasma-specific PCR to detect the presence of the pathogen. <strong>Table S6.</strong> Detailed information on the leafhoppers incubated with strawberry plants during the phytoplasma transmission assays. <strong>Table S7.</strong> Detailed information on <em>Macosteles quadrilineatus</em> used to study the leafhopper microbiome. <strong>Table S8. </strong>Detailed information on the insecticides used by strawberry growers during both grow seasons included in the study and those treatments selected for further statistic analyses. <strong>Table S9. </strong>Identification and number of leafhopper species captured in strawberry fields in each geographic region screened in this study. <strong>Table S10. </strong>Detailed information of diversity indexes Shannon and Simpson calculated using the data collected in this study. <strong>Table S11.</strong> Fixed days and temperature values used during leafhopper populations modelling. <strong>Table S12.</strong> Detailed information on the taxonomy of the phytoplasma strain SbGPQ affecting strawberry plants in eastern Canada by hybridization and illumine sequencing and by PCR amplification, cloning and Sanger sequencing. <strong>Table S13.</strong> Detailed information on <em>Macosteles quadrilineatus</em> microbiome including OTUs, reads, and metadata information. <strong>Table S14.</strong> Detailed information on the core microbiome for <em>Macosteles quadrilineatus</em> captured during each growing season and in common for all the leafhoppers analyzed during this study.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.080
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0620.000

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 head0.058
GPT teacher head0.266
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes2
Has abstractyes

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