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Record W4317820117 · doi:10.1093/conphys/coac084

Supplementation in vitamin B3 counteracts the negative effects of tryptophan deficiencies in bumble bees

2022· article· en· W4317820117 on OpenAlexaff
Mathilde L. Tissier, Stéphane Kraus, Tamara Gómez‐Moracho, Mathieu Lihoreau

Bibliographic record

VenueConservation Physiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsBishop's University
FundersCollege of Natural Resources and Sciences, Humboldt State UniversityCentre National de la Recherche ScientifiqueAssociation Nationale de la Recherche et de la TechnologieEuropean Commission
KeywordsBiologyTryptophanVitaminEcologyZoologyEndocrinologyBiochemistryAmino acid

Abstract

fetched live from OpenAlex

Abstract Increasing evidence highlights the importance of diet content in nine essential amino acids for bee physiological and behavioural performance. However, the 10th essential amino acid, tryptophan, has been overlooked as its experimental measurement requires a specific hydrolysis. Tryptophan is the precursor of serotonin and vitamin B3, which together modulate cognitive and metabolic functions in most animals. Here, we investigated how tryptophan deficiencies influence the behaviour and survival of bumble bees (Bombus terrestris). Tryptophan-deficient diets led to a moderate increase in food intake, aggressiveness and mortality compared with the control diet. Vitamin B3 supplementation in tryptophan-deficient diets tended to buffer these effects by significantly improving survival and reducing aggressiveness. Considering that the pollens of major crops and common plants, such as corn and dandelion, are deficient in tryptophan, these effects could have a strong impact on bumble bee populations and their pollination service. Our results suggest planting tryptophan and B3 rich species next to tryptophan-deficient crops could support wild bee populations.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.688

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

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