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Record W2962179143 · doi:10.7717/peerj.7323

Predicting the potential distribution of the Asian citrus psyllid, <i>Diaphorina citri</i> (Kuwayama), in China using the MaxEnt model

2019· article· en· W2962179143 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePeerJ · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytoplasmas and Hemiptera pathogens
Canadian institutionsnot available
Fundersnot available
KeywordsDiaphorina citriChinaGeographyYangtze riverDistribution (mathematics)SeasonalityPEST analysisQuarter (Canadian coin)Air temperatureEcologyEnvironmental scienceMeteorologyBiologyMathematics

Abstract

fetched live from OpenAlex

Background Citrus huanglongbing (HLB) is a destructive disease of citrus and a major threat to the citrus industry around the world. This disease accounts for substantial economic losses in China every year. Diaphorina citri Kuwayama is one of the major vectors by which citrus HLB is spread under natural conditions in China. Research is needed to identify the geographic distribution of D. citri and its major areas of occurrence and to formulate measures for early warning, monitoring, and control of this pest and citrus HLB. Methods In this study, the ecological niche modelling software MaxEnt (maximum entropy model) was combined with ArcGIS (a geographic information system) to predict the potential geographic distribution of D. citri in China. Key environmental factors and the appropriate ranges of their values were also investigated. Results Our results show that the training data provided a good forecast (AUC mean = 0.988). The highly suitable areas for D. citri in China are mainly concentrated to the south of the Yangtze River, and the total area is 139.83 × 10 4 km 2 . The area of the moderately suitable areas is 27.71 × 10 4 km 2 , with a narrower distribution than that of the highly suitable area. The important environmental factors affecting the distribution of D. citri were min temperature of coldest month, mean temperature of coldest quarter, precipitation of wettest quarter, mean temperature of warmest quarter, precipitation of warmest quarter, max temperature of warmest month, and temperature seasonality. These results provide a valuable theoretical basis for risk assessments and control of D. citri . Discussion The predicted results showed that there were highly suitable areas for D. citri in Chongqing, Hubei, Anhui, and Jiangsu. Therefore, the possibility exists for the further spread of D. citri in China in the future. Extreme temperature variables, especially the min temperature of the coldest month, play an important role in the distribution of D. citri and are most closely related to the distribution of D. citri .

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.193

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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