MétaCan
Menu
Back to cohort
Record W4309473259 · doi:10.1080/09670874.2022.2145521

First record of damage by the red palm weevil, <i>Rhynchophorus ferrugineus</i> (Olivier, 1790) in sugarcane fields in China

2022· article· en· W4309473259 on OpenAlexaff
Zhen‐Qiang Qin, De‐Wei Li, Ya‐Wei Luo, Xing Huang, François R. Goebel, Zhongshi Zhou

Bibliographic record

VenueInternational Journal of Pest Management · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDate Palm Research Studies
Canadian institutionsMinistry of Agriculture
FundersNatural Science Foundation of Guangxi ProvinceNational Natural Science Foundation of China
KeywordsRhynchophorusWeevilPalmBiologyCanePEST analysisChinaHorticultureInfestationAgronomyToxicologyBotanyGeographySugarArchaeology

Abstract

fetched live from OpenAlex

The red palm weevil, Rhynchophorus ferrugineus is one of the alien species of high risk in China and a key pest of palm trees. This paper presents the first status report of the damage caused to sugarcane by R. ferrugineus in China. The first observation of damage was recorded in Changling farm in Shangsi County, Guangxi Province, on a small plot in October 2017 and the recorded damage level, exceeded 80% with cane stalks completely destroyed. In September 2021, R. ferrugineus was found in sugarcane fields (11.85 hm2) in Liucheng County, in Liuzhou City, but the percentage of plant damaged was sporadic and didn’t exceed 1%. The mean percent of damaged stalks and the length of damaged tunnels in plants recorded were 0.76%, and 19.54 cm, respectively when the canes were harvested. This discovery allowed us to prevent potential establishment and spread of the red palm weevil in the sugarcane regions in China, as well as serve a warning notice for other sugarcane regions worldwide.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.246
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 source (direct Gemma or distilled Codex), 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Pest ManagementSame topicDate Palm Research StudiesFrench-language works237,207