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Record W1832092009 · doi:10.1139/cjb-2012-0169

Species-specific environmental requirements to break seed dormancy: implications for selection of regeneration niches in three <i>Lonicera</i> (Caprifoliaceae) species

2012· article· en· W1832092009 on OpenAlexvenueno aff
Alejandro Santiago, José M. Herranz, Elena Copete, Pablo Ferrandis

Bibliographic record

VenueBotany · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsDormancyBiologyGerminationBotanySeed dormancyCaprifoliaceaeEcological nicheChilling requirementNicheHabitatEcology

Abstract

fetched live from OpenAlex

Environmental requirements for seed germination can operate as an important filter in determining the regeneration niche and ultimately the habitat preference of many plant species. We hypothesize that morphological and morphophysiological seed dormancy may play a major role in habitat selection, because underdeveloped embryos responsible for those dormancy types usually require strict species-specific environmental conditions to grow and to overcome dormancy, imposing marked constraints to recruitment and thus to species distribution. We analyzed the influence of temperature and light on embryo growth and seed germination, as well as germination phenology in three Lonicera (Caprifoliaceae) species. Lonicera xylosteum L. seeds had morphological dormancy. Those of Lonicera etrusca Santi had unusual within-species dormancy variability, with a fraction being able to show both morphological and morphophysiological dormancy. Seeds of Lonicera arborea had deep complex morphophysiological dormancy. The close correspondence between the environmental conditions that each Lonicera species requires to break seed dormancy and their altitudinal range suggests that morphological and morphophysiological dormancies act as important filters in determining the regeneration niches of species, probably because such dormancy mechanisms impose markedly specific environmental requirements during the earlier stages of recruitment.

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 score0.692
Threshold uncertainty score0.216

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 head0.071
GPT teacher head0.233
Teacher spread0.162 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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