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Record W6921149990 · doi:10.6084/m9.figshare.4901786

An assessment of the diversity of ground-dwelling invertebrates in three urban land-use types in central British Columbia

2017· article· en· W6921149990 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFaunaContext (archaeology)BiodiversityInvertebrateGeorge (robot)Sympatric speciationDiversity (politics)Ecosystem

Abstract

fetched live from OpenAlex

Ground dwelling arthropods and other sympatric invertebrates in urban environments often exist completely unnoticed yet are known to provide important ecosystem services1,2,3. The central interior of British Columbia is largely unsurveyed for many taxa, and its urban centers have been particularly ignored. Prince George (population ~75000) is the main industrial and service hub for much of the interior of British Columbia north of Kamloops. Due to rapid industrialization of the central and northern interior of British Columbia, Prince George is also experiencing substantial growth in economic activity and population. Development decisions in this context should be based on sound ecological data, yet little to no urban arthropod biodiversity data exist for the region. We monitored arrays of pitfall traps in three land-use types (residential, greenbelt, industrial; N = 4 for each) on a near-weekly basis during the summer of 2015 in Prince George, British Columbia. Initial sorting and morphospecies-based DNA barcoding has revealed over 180 species as a conservative estimate for γ-diversity. Several groups – such as <i>Megaselia</i> spp. flies (Phoridae), platygastrid wasps, and spiders – show substantial levels of diversity. We are currently working on completing a pictorial catalog of the local fauna to aid in our ongoing sorting and detailed assessment of assemblages found at each land-use type.

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)0.000
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 score0.668
Threshold uncertainty score0.748

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 head0.047
GPT teacher head0.280
Teacher spread0.233 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicForensic Entomology and Diptera StudiesFrench-language works237,207