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Effects of canopy on the sapling composition and structure in a subalpine old-growth forest, central Japan

2000· article· en· W1488159806 on OpenAlexvenueno aff
Kyoko Kato, Shin‐Ichi Yamamoto

Bibliographic record

VenueEcoscience · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCanopyEvergreenDeciduousEcologySubalpine forestBotanyBiologyEcosystem

Abstract

fetched live from OpenAlex

Species composition of the sapling bank and size structure of Abies saplings under different canopy conditions, such as the evergreen coniferous canopy of Abies spp. (evergreen canopy), the deciduous broad-leaved canopy of Betula spp. (deciduous canopy), and canopy gaps were examined in a subalpine old-growth forest of the northern Yatsugatake mountains, central Japan. Light levels tended to be lowest under evergreen canopies, higher under deciduous canopies, and highest in canopy gaps. The species composition and structure of conifer sapling (≥ 15 cm tall and dbh < 5 cm) banks varied with the canopy conditions. Abies mariesii saplings were the exclusive dominants under both evergreen and deciduous canopies and in canopy gaps. A. veitchii saplings were subdominant, and Tsuga diversifolia and Picea jezoensis var. hondoensis saplings were few under all canopy conditions. In canopy gaps, the relative density of A. veitchii saplings was highest and that of A. mariesii saplings was lowest under canopy conditions. A. veitchii saplings might thus outcompete A. mariesii saplings in canopy gaps. The sapling height distributions of Abies spp. were different depending on light conditions; they were L- or reverse J-shaped at lower light levels and bell-shaped at higher light levels. Species composition of the sapling bank and the size structure of Abies saplings are considered to be influenced not only by the difference between closed canopy and canopy gaps, but also by the difference between evergreen and deciduous canopies due to the different light conditions.

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 score0.014
Threshold uncertainty score0.159

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 head0.002
GPT teacher head0.184
Teacher spread0.181 · 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

Citations21
Published2000
Admission routes1
Has abstractyes

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