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Record W1972959604 · doi:10.1139/b11-024

Local differentiation of flowering phenology in an alpine-snowbed herb <i>Gentiana nipponica</i>

2011· article· en· W1972959604 on OpenAlexvenueno aff
Yuka Kawai, Gaku Kudo

Bibliographic record

VenueBotany · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceMinistry of Environment
KeywordsPhenologySnowmeltBiologyPopulationGentianaHabitatBotanyLocal adaptationEcologyGrowing seasonHorticultureDemography

Abstract

fetched live from OpenAlex

Phenological events of alpine plants are strongly influenced by the seasonal thermal conditions at the local scale that are caused by heterogeneity in snowmelt time. Populations in late-snowmelt locations suffer from a short period for seed maturation, in which rapid flowering that occurs soon after snowmelt (i.e., a low thermal requirement for flowering) is advantageous. To test the possibility of local adaptation of flowering phenology, we compared reproductive phenology, the preflowering period, and the thermal requirement for flowering of the alpine-snowbed species Gentiana nipponica Maxim. between populations inhabiting early- and late-snowmelt sites within a local area. Plants in the late-snowmelt population showed a shorter preflowering period than in the early-snowmelt population; nevertheless, they often failed to set fruit owing to the short growth period. To test the plasticity and genetic regulation of flowering phenology, we performed a reciprocal transplant experiment between early- and late-snowmelt populations. Although the preflowering period showed clear plastic responses depending on the transplanted habitats, the lower thermal requirement for flowering was retained in plants originating from the late-snowmelt populations, even after transplantation. Therefore, habitat-specific differentiation of flowering phenology was genetically determined between these local 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.912

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 head0.055
GPT teacher head0.212
Teacher spread0.157 · 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

Citations28
Published2011
Admission routes1
Has abstractyes

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