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Record W2578857700 · doi:10.1098/rsos.160538

Fluff-thieving birds sabotage seed dispersal

2017· article· en· W2578857700 on OpenAlexafffund
Vanya G. Rohwer, Anton Pauw, Paul R. Martin

Bibliographic record

VenueRoyal Society Open Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsQueen's University
FundersSociety of Canadian OrnithologistsCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaAmerican Ornithologists' Union
KeywordsSeed dispersalNest (protein structural motif)Mutualism (biology)Biological dispersalEcologyBiologyPlant species

Abstract

fetched live from OpenAlex

Characterizing many species interactions as mutualisms can be misleading because some members of the interaction derive greater fitness benefits at the expense of other members. We provide detailed natural history data on a suspected bird–plant mutualism in South Africa where many species of birds use fluffy Eriocephalus seed material to construct their nests, potentially dispersing seeds for the plant. We focus on a common bird, Prinia maculosa , which invests heavily in gathering Eriocephalus material. Prinias spent 5 of their median 6-day nest construction period adding seed material to their nests and frequently travelled outside their territory boundary to gather Eriocephalus material. Yet, prinias gathered primarily Eriocephalus fluff and actively avoided gathering seeds. The average prinia nest contained only 6.6 seeds, but contained fluff from 579 seeds. These data suggest that prinias provide limited dispersal benefits to Eriocephalus plants. By contrast, the large amounts of Eriocephalus fluff in prinia nests, and the effort that prinias invest in gathering it, suggest that prinias benefit from constructing their nests with Eriocephalus material. We end by outlining hypotheses for possible fitness benefits that Eriocephalus material could provide prinias and other birds.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0030.002
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 head0.050
GPT teacher head0.275
Teacher spread0.225 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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