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Record W2017554245 · doi:10.1139/b09-108

Factors affecting the production, growth, and survival of sprouting stems in the multi-stemmed understory shrub <i>Lindera triloba</i>

2010· article· en· W2017554245 on OpenAlexvenueno aff
Michinari Matsushita, Nobuhiro Tomaru, Daisuke Hoshino, Naoyuki Nishimura, Shin‐Ichi Yamamoto

Bibliographic record

VenueBotany · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsUnderstoryShrubBiologyCompetition (biology)BotanyMyrmecophyteCrowdingEcologyCanopy

Abstract

fetched live from OpenAlex

We investigated the structure and dynamics of the multi-stemmed understory shrub Lindera triloba (Sieb. et Zucc.) Blume over 3 years in an old-growth coniferous forest, and quantitatively evaluated the factors affecting the ramet production, growth, and survival. Most genets sprouted continuously and exhibited multiple-stemmed structures with a few large and many small ramets. The skewed ramet-size distribution within genets resulted from the local crowding of neighboring trees, but not from the number of ramets within genets. This indicated that inter-plant competition is asymmetric (i.e., larger individuals outcompete one-sidedly smaller ones), but intra-plant competition (i.e., competition among ramets within genets) is symmetric (i.e., smaller ones also competitively affect larger ones). The local crowding of neighboring understory trees consistently negatively affected the ramet production, growth, and survival of L. triloba. Intra-genet crowding (i.e., crowding of ramets within genets) also negatively affected the ramet dynamics. On the other hand, the largest-ramet size within genets had positive relationships with the ramet dynamics, indicating that physiological integration within genets plays a role as supporting younger ramets. Based on our results, to fully understand genet persistence strategies in clonal shrub species, it is important to consider the effects of intra-genet crowding and modular integration, as well as plant-to-plant interaction.

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)0.001
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 score0.008
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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

Citations16
Published2010
Admission routes1
Has abstractyes

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