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Record W2376885521

Main Invasive Species and Their Control Strategies in Zhejiang Province

2007· article· en· W2376885521 on OpenAlexaboutno aff
Jianbo Lu

Bibliographic record

VenueBulletin of Science and Technology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyBiodiversityEichhornia crassipesInvasive speciesPopulationAgroforestryEnvironmental protectionForestryEcologyBiologyAquatic plant
DOInot available

Abstract

fetched live from OpenAlex

As an opening region with high population and booming foreign trade in seaboard,Zhejiang Province now is being suffered from a serious biological invasions of waterhyacinth(Eichhornia crassipes),pine wood nematode(Bursaphelenchus xylophilus),smooth cord-grass(Spartina alterniflor),canada goldenrod(Solidago Canadensis),et al.since 1990.Waterhyacinth has been the primary invasive specie and put threats to navigation,citizen and environment in Hangjiahu plain,Ningshao plain and Wentai plain river system networks.Pine wood nematode,which was found in 1991,overruns in Beilun County,Xiangshan County and Ninghai County of Ningbo region,Dinghai County,Putuo County,Daishan County and Shensi County of Zhoushan region,Xihu County and Fuyang County of Hangzhou region,Wuxing County and Changxing County of Huzhou region and Pinghu County of Jiaxing region.Now pine wood nematode has been one of serious invasive species and threatens to the biodiversity and ecological safety in Zhejiang Province.Therefore,based on investigation and much data collection,this article analyzes the current situations of main invasive species in Zhejiang Province,summarizes the technique measures of preventing biological invasions and then puts forward the long-term strategies which could be helpful for protecting the biodiversity and promoting the sustainable development of agriculture,forestry,livestock and fishery in Zhejiang Province.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.981

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 studies0.0000.003
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 head0.006
GPT teacher head0.175
Teacher spread0.169 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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