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Record W6929904344 · doi:10.5061/dryad.4d12c92

Data from: Consumer-resource interactions along urbanization gradients drive natural selection

2018· dataset· en· W6929904344 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2018
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUrbanizationNatural selectionHabitatGallPredationAdaptation (eye)

Abstract

fetched live from OpenAlex

Urbanization is an important component of global change. Urbanization affects species interactions, but the evolutionary implications are rarely studied. We investigate the evolutionary consequences of a common pattern: the loss of high trophic-level species in urban areas. Using a gall-forming fly, Eurosta solidaginis, and its natural enemies that select for opposite gall sizes, we test for patterns of enemy loss, selection, and local adaptation along five urbanization gradients. Eurosta declined in urban areas, as did predation by birds, which preferentially consume gallmakers that induce large galls. These declines were linked to changes in habitat availability, namely reduced forest cover in urban areas. Conversely, a parasitoid which attacks gallmakers that induce small galls was unaffected by urbanization. Changes in patterns of attack by birds and parasitoids resulted in stronger directional selection, but loss of stabilizing selection in urban areas, a pattern which we suggest may be general. Despite divergent selective regimes, gall size did not very systematically with urbanization, suggesting but not conclusively demonstrating that environmental differences, gene flow, or drift, may have prevented the adaptive divergence of phenotypes. We argue that the evolutionary effects of urbanization will have predictable consequences for patterns of species interactions and natural selection.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.044
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0440.032

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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2018
Admission routes1
Has abstractyes

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