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Record W4393074931 · doi:10.1139/as-2022-0034

Effects of short- and long-term experimental warming on plant–pollinator interactions and floral rewards in the Low Arctic

2024· article· en· W4393074931 on OpenAlexvenueno aff
Roxaneh S. Khorsand, Flavia Sancier‐Barbosa, Jeremy L. May, Toke T. Høye, Steven F. Oberbauer

Bibliographic record

VenueArctic Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsShrubPollinatorEvergreenForbPhenologyBiologyDeciduousPollenNectarPollinationTundraEcologyEcosystemGrassland

Abstract

fetched live from OpenAlex

Plant phenological and growth responses to experimental warming are widely documented, but less is known about warming effects on plant–pollinator interactions. We investigated the effects of short- and long-term passive warming on flowering phenology, insect visitation, fruit production, and floral rewards in the Low Arctic in northern Alaska. To better understand the role of insect visitors in plant reproductive success, we quantified pollen loads on floral visitors and tested for pollen limitation in four species. Long-term warming advanced flowering onset in evergreen shrubs and forbs. Warming, in general, increased the duration of flowering for forbs, evergreen shrubs, and deciduous shrubs. Considering all growth forms together, long-term warming increased floral density. This pattern was primarily driven by deciduous and evergreen shrubs. Dipterans accounted for more visits than Hymenopterans, although Hymenopterans had higher pollen loads. Insect exclusion and warming decreased fruit set in the forb, Bistorta officinalis Delarbre. Nectar volume in the deciduous shrub, Vaccinium uliginosum, was higher in the warmed plots than the control, but nectar quality did not differ. Advanced flowering onset, longer flowering duration, and increased flower density and nectar volume may have important implications for the pollinator community, warranting further research on long-term warming effects on tundra ecosystems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.148

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 head0.034
GPT teacher head0.273
Teacher spread0.240 · 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 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

Citations3
Published2024
Admission routes1
Has abstractyes

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