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Record W2756724328 · doi:10.1139/cjb-2017-0054

Interannual variation in bryophyte dispersal: linking bryophyte phenophases and weather conditions

2017· article· en· W2756724328 on OpenAlexaffvenue
Marion Barbé, Nicole J. Fenton, Richard T. Caners, Yves Bergeron

Bibliographic record

VenueBotany · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBryophyte Studies and Records
Canadian institutionsRoyal Alberta MuseumUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsBryophytePropaguleBiological dispersalPhenologyBiologyEcologyContext (archaeology)PopulationDemography

Abstract

fetched live from OpenAlex

In the context of global changes that modify the distribution range of species, there is an urgent need to identify climate variables affecting species dispersal. We investigated patterns of aerial propagule release (sexual and asexual) of boreal bryophytes in response to weather. We present the first community-level study that examines the impact of weather on the phenology of bryophytes, and we divided it into phases. Bryophyte propagule rain was trapped in 2013 (summer and fall) and 2014 (spring and fall), and climatic variables were collated from the years 2012 to 2014. The phases of the phenology and the weather variables one season to two years preceding propagule release, which may influence the dispersal of propagules, were identified. Propagule release varies with weather conditions at the time of dispersal (direct effects), but is also associated with weather during the winter and summer one year preceding dispersal (indirect effects), which presumably influences survival, growth, and fertilization of the mother plant as well as propagule and sporophyte development. We suggest that propagule release is related to weather conditions occurring from one to several previous seasons, particularly humidity, temperature, and length (duration) of winter. Dividing the phenology into phases, we present an innovative method that should provide new insights into bryophyte dispersal dynamics in response to climate.

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 score0.696
Threshold uncertainty score0.459

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 studies0.0010.000
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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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