MétaCan
Menu
Back to cohort
Record W2321603592 · doi:10.1021/jf500521w

Secondary Compounds in Floral Rewards of Toxic Rangeland Plants: Impacts on Pollinators

2014· review· en· W2321603592 on OpenAlexaff
Rebecca E. Irwin, Daniel Cook, Leif L. Richardson, Jessamyn S. Manson, Dale R. Gardner

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2014
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Alberta
FundersNational Science Foundation
KeywordsPollinatorNectarPollinationPollenBiologyZoophilyHerbivoreGeneralist and specialist speciesPollen sourceRangelandPlant reproductionEcologyBotanyHabitat

Abstract

fetched live from OpenAlex

The study of plant secondary chemistry has been essential in understanding plant consumption by herbivores. There is growing evidence that secondary compounds also occur in floral rewards, including nectar and pollen. Many pollinators are generalist nectar and pollen foragers and thus are exposed to an array of secondary compounds in their diet. This review documents secondary compounds in the nectar or pollen of poisonous rangeland plants of the western United States and the effects of these compounds on the behavior, performance, and survival of pollinators. Furthermore, the biochemical, physiological, and behavioral mechanisms by which pollinators cope with secondary compound consumption are discussed, drawing parallels between pollinators and herbivores. Finally, three avenues of future research on floral reward chemistry are proposed. Given that the majority of flowering plants require animals for pollination, understanding how floral reward chemistry affects pollinators has implications for plant reproduction in agricultural and rangeland habitats.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.237
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations100
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural and Food ChemistrySame topicPlant and animal studiesFrench-language works237,207