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Record W6969753804 · doi:10.5683/sp3/tayp6v

Data: fruit size of Indomalayan plants

2022· dataset· en· W6969753804 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldEnvironmental Science
TopicHydrological Forecasting Using AI
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFrugivoreTraitBiological dispersalSeed dispersalTaxonPlant evolutionSeed dispersal syndromeEvolutionary dynamics

Abstract

fetched live from OpenAlex

Abstract Community trait assembly, the formation of distributions of phenotypic characteristics across coexisting species, can occur via two main processes: the filtering of trait distributions in the regional pool and in situ phenotypic evolution in local communities. But the relative importance of these processes remains unclear, largely because of the difficulty in determining the timing of evolutionary trait changes and biogeographic dispersal events in phylogenies. We assessed evolutionary and biogeographic transitions in woody plant species across the Indo-Malay Archipelago, a series of island groups where the same plant lineages interact with different seed disperser and seed predator assemblages. Fruit size in 2650 taxa spanning the angiosperm tree-of-life tended to be smaller in the Sulawesi and Maluku island groups, where frugivores are less diverse and smaller-bodied, than in the regional source pool. While numerous plant lineages (not just small-fruited ones) reached the isolated islands, colonists tended to be the smaller-fruited members of each clade. Nearly all evolutionary transitions to smaller fruit size predated, often substantially, organismal dispersal to the islands. Our results suggest that filtering rather than within-island evolution largely determined the distribution of fruit sizes in these regions.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.015

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 head0.044
GPT teacher head0.276
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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