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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 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 categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

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 science0.0030.004
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 head0.020
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 teacher head, not a consensus.

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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