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Record W2136602562 · doi:10.1139/x09-158

Individual-tree growth dynamics of mature Abies alba during repeated irregular group shelterwood (Femelschlag) cuttings

2009· article· en· W2136602562 on OpenAlexvenueno aff
Klaus J. Puettmann, Anthony W. D’Amato, Ulrich Kohnle, Jürgen Bauhus

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsAbies albaCuttingForestrySite indexPostharvestHorticultureStand developmentBiologyBotanyEnvironmental sciencePicea abiesGeography

Abstract

fetched live from OpenAlex

This study investigated the individual-tree diameter response of mature silver fir ( Abies alba Mill.) to reproduction harvests (Femelschlag: an irregular group shelterwood method) on six sites in the Black Forest, Germany. On each site, four different treatments were applied, including a control treatment and short-, medium, and long-term regeneration periods aimed at the complete removal of overstory trees within 20, 35, and 50 years, respectively. These treatments created a wide variety of growing conditions for individual trees. Relationships between relative diameter growth and stand-level and neighborhood interaction indices were evaluated. Growing conditions for individual trees in control conditions were best characterized using Lorimer’s index for a 16 m radius neighborhood. Equations predicting tree growth in control stands underpredicted initial growth of trees after harvesting operations, suggesting a release effect that is not captured by postharvest density. This effect was larger for smaller trees and influenced by removal intensity. Growth response to density reductions was also influenced by previous harvests. Our results suggest that the growth response of mature trees to reproduction harvests may become an important consideration when increased emphasis is placed on managing for long-term regeneration periods.

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)0.002
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 score0.220
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations42
Published2009
Admission routes1
Has abstractyes

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