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Record W1955950099 · doi:10.1111/1556-4029.12869

Blow Flies from Forest Fragments Embedded in Different Land Uses: Implications for Selecting Indicators in Forensic Entomology

2015· article· en· W1955950099 on OpenAlexaff
Mirian S. de Souza, Mateus Pepinelli, Eduardo C. de Almeida, José M. Ochoa-Quintero, Fábio de Oliveira Roque

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsUniversity of Toronto
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsForensic entomologyEcologyBiodiversityAbundance (ecology)CalliphoridaeLand coverVegetation (pathology)Landscape ecologyBioindicatorPrincipal component analysisEntomologyGeographyBiologyLand useForestryHabitat

Abstract

fetched live from OpenAlex

Given the general expectation that forest loss can alter biodiversity patterns, we hypothesize that blow fly species abundances differ in a gradient of native vegetation cover. This study was conducted in 17 fragments across different landscapes in central Brazil. Different land cover type proportions were used to represent landscape structure. In total, 2334 specimens of nine species of Calliphoridae were collected. We used principal component analysis (PCA) to reduce dimensionality and multicollinearity of the landscape data. The first component explained 70%, and it represented a gradient of forest-pasture land uses. Alien species showed a wide distribution in different fragments with no clear relationship between the abundance values and the scores of PCA axes, whereas native species occurred only in areas with a predominance of forest cover. Our study revealed that certain native species may be sensitive to forest loss at the landscape scale, and they represent a bioindicator in forensic entomology.

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)0.001
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 score0.056
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.053
GPT teacher head0.298
Teacher spread0.245 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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