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Record W4400057070 · doi:10.3389/fsufs.2024.1411410

Dietary foundations for pollinators: nutritional profiling of plants for bee health

2024· article· en· W4400057070 on OpenAlexaff
Khara W. Stephen, Katherine D. Chau, Sandra M. Rehan

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
Fundersnot available
KeywordsPollinatorProfiling (computer programming)BiologyPollenEcologyPollinationComputer science

Abstract

fetched live from OpenAlex

Human activities, including urban expansion, intensive farming practices, and the application of pesticides have significantly reshaped bee habitats. Understanding the nutritional content of pollen, the primary source of bees’ proteins and lipids, is important for maintaining their diet and health. In this study, we set out to determine the nutritional composition of pollen from various plant families and genera. Our objectives were to analyze the levels of non-esterified fatty acids (NEFAs), amino acids (AAs), protein-to-lipid (P:L) ratios, and omega-6:3 ratios of 57 pollen species native to North America. These data suggest a potential trade-off between NEFA and AA content within pollen, suggesting that a diverse floral diet may benefit bees more than a single pollen source. The AA profiles showed considerable diversity, with all pollen species providing the essential amino acids (EAAs) required for bee health, except for methionine which was lacking in Rhus glabra pollen. The plant family Asteraceae was especially abundant in EAAs. P:L ratios varied widely further emphasizing bees’ need for access to a diverse array of nutritional profiles. There were no overall nutritional differences between pollen from native and introduced plant species. This study highlights the significance of a diverse array of floral resources to meet the comprehensive nutritional needs of bees, contributing to the support of pollinator populations and the broader ecological system.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.195

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 science0.0000.000
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 head0.052
GPT teacher head0.265
Teacher spread0.213 · 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 designNot applicable
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

Citations18
Published2024
Admission routes1
Has abstractyes

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