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Record W2900155409 · doi:10.1139/gen-2018-0062

DNA barcoding reveals insect diversity in the mangrove ecosystems of Hainan Island, China

2018· article· en· W2900155409 on OpenAlexvenueno aff
Lu Liu, Zixiao Guo, Cairong Zhong, Suhua Shi

Bibliographic record

VenueGenome · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMangroveDNA barcodingBiologyEcologyThreatened speciesBiodiversityCytochrome c oxidase subunit ITaxonSpecies diversityMitochondrial DNAHabitat

Abstract

fetched live from OpenAlex

Insect diversity is an indicator of environmental conditions. Frequent outbreaks of mangrove pests have threatened the fragile mangrove ecosystem in China and the sustainable utilization of mangrove resources. The understanding of mangrove pests, as well as a fundamental knowledge of insect diversity, in mangrove forests in China has been hindered by the difficulty of morphological species delimitation because captured insect specimens are either larvae or incompletely preserved adults. DNA barcoding technology uses only a small amount of DNA to conduct species identification. Taking advantage of this, we investigated the entomofauna of mangrove forests on Hainan Island by using a barcode combining cytochrome c oxidase subunit I (COI) and cytochrome-b (Cytb). We collected 627 specimens at six localities around the island, which were identified as 219 insect species belonging to 11 orders and 72 families. Lepidoptera, Coleoptera, and Hymenoptera are the most species-rich and abundant taxa. We also identified 13 mangrove pests, 5 parasitoids, and 12 species of predators.

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 score0.052
Threshold uncertainty score0.988

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 science0.0000.001
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 head0.014
GPT teacher head0.200
Teacher spread0.186 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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