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Record W2794464238 · doi:10.11646/zoosymposia.12.1.8

<strong>Carabid and spider population dynamics on urban green roofs</strong>

2018· article· en· W2794464238 on OpenAlexaffabout
J. A. Colin Bergeron, Jaime Pinzón, John R. Spence

Bibliographic record

VenueZoosymposia · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsNatural Resources CanadaUniversity of Alberta
FundersUniversities Space Research Association
KeywordsBiological dispersalEcologyHabitatSpecies richnessGround beetleSpiderGeographyThreatened speciesBiodiversityGreen roofAbundance (ecology)PopulationBiologyRoof

Abstract

fetched live from OpenAlex

Green roofs are valuable ecosystems that enhance the biodiversity value of urban landscapes in northern Alberta. Using pitfall traps on green roofs and adjacent ground sites, we show that roof arthropods are characteristic of native grasslands that are threatened in Alberta. Although we found lower abundance of spiders and carabids on roofs, species richness as assessed by rarefaction did not differ between roof and nearby ground sites. Thus, arthropod communities of these extensive green roofs do not seem to be impoverished compared to ground habitats, despite differences in local environmental variables (e.g. substrate depth, surface, vertical isolation). Seasonal distribution of larval and adult captures in pitfall traps, and observation of egg sacs in spiders suggest that a number of species have established reproducing populations on these green roofs. Interestingly, carabid assemblages differed markedly in species composition between roofs and ground sites, but spider assemblages were much more similar. We explain this in relation to differences in dispersal ability between these taxa. Green roofs are likely valuable for urban conservation allowing native species characteristic of native grasslands to permeate through urban landscapes.

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 score0.040
Threshold uncertainty score0.916

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)0.0000.001

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 head0.007
GPT teacher head0.226
Teacher spread0.218 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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