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Record W4238210573 · doi:10.7287/peerj.preprints.1977

Do plant secondary metabolites help drive avian granivore seed selection?

2016· preprint· en· W4238210573 on OpenAlexaboutno aff
Christopher J. Whelan, Diya Majumdar, Joel S. Brown, Amy E Hank, Andrea Iorgovan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologySparrowWildflowerNative plantHerbivoreForagingFrugivoreIntroduced speciesEcologyHabitat

Abstract

fetched live from OpenAlex

Plant secondary metabolites (PSMs), found virtually universally throughout the plant kingdom, function in myriad ways, including defense against enemies, attraction of pollinators, communication between plants, and protection against various abiotic stressors. Extensive research has examined how PSMs mediate interactions between plants and herbivores and plant and frugivores. In contrast, little research has investigated their potential role in defense against granivores. In two seed selection experiments, we quantified seed preference of house sparrows and native granivores, respectively, when offered each of 10 native seeds and proso millet, a commercial bird seed. House sparrows and native granivores greatly preferred millet over all offered native seeds. House sparrows largely rejected seeds of all five wildflower species, but native granivores preferred three of the five wildflowers. House sparrows readily consumed seeds of all five native grass species, but native granivores rejected Canada rye. House sparrows and native granivores both rejected seeds of Illinois bundle flower. Although seed preferences in the non-native house sparrow and native granivores differed significantly, we have found no consistent relationship between seed selection and presence or absence of classes of plant secondary metabolites.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.029
GPT teacher head0.211
Teacher spread0.182 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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