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Record W2900463674 · doi:10.1002/ajb2.1189

Patterns and predictors of fleshy fruit phenology at five international botanical gardens

2018· article· en· W2900463674 on OpenAlexaff
Amanda S. Gallinat, Richard B. Primack, Charles G. Willis, Birgit Nordt, Albert‐Dieter Stevens, Robert T. Fahey, Alan T. Whittemore, Yanjun Du, Zoe A. Panchen

Bibliographic record

VenueAmerican Journal of Botany · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
FundersArnold ArboretumNational Science Foundation
KeywordsPhenologyBiologyFructificationEcologyTemperate climateBotanyHorticulture

Abstract

fetched live from OpenAlex

PREMISE OF THE STUDY: To improve our understanding of the patterns and drivers of fleshy fruit phenology, we examined the sequence, patterns across years and locations, and drivers of fruiting times at five botanical gardens on three continents. METHODS: We monitored four stages of fruit phenology for 406 temperate, fleshy-fruited, woody plant species in 2014 and 2015. KEY RESULTS: Across all gardens, ripe fruits were present from May to March of the following year, with peak fruiting durations ranging from under 1 week to over 150 days. Species-level first fruiting and onset of peak fruiting dates were strongly associated with one another within sites and were more consistent between years and sites than the end of peak fruiting and last fruiting date. The order of fruiting among species between years and gardens was moderately consistent, and both peak fruiting times and fruiting durations were found to be phylogenetically conserved. CONCLUSIONS: The consistent order of fruiting among species between years and locations indicates species-specific phenological responses to environmental conditions. Wide variation in fruiting times across species and in the duration of peak fruiting reinforces the importance of understanding how plant phenology impacts dispersers and monitoring the health and consistency of these interactions.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.424

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 head0.220
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 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

Citations20
Published2018
Admission routes1
Has abstractyes

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