MétaCan
Menu
Back to cohort
Record W1987878199 · doi:10.1139/x03-197

Dynamics of Norway spruce and silver fir natural regeneration in a mixed stand under uneven-aged management

2004· article· en· W1987878199 on OpenAlexvenueno aff
Giacomo Grassi, Gianfranco Minotta, Giustino Tonon, U. Bagnaresi

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
FundersInterregRegione del VenetoEuropean Commission
KeywordsAbies albaRegeneration (biology)Picea abiesNatural regenerationCanopyUnderstoryStand developmentBiologyForestryKarstEcologySilvicultureBotanyGeography

Abstract

fetched live from OpenAlex

In a 0.75-ha plot in a Norway spruce (Picea abies Karst.) – silver fir (Abies alba Miller) stand in Comelico (Italian eastern Alps), we analysed (i) the distribution and growth of natural regeneration of Norway spruce and silver fir as affected by stand structure and (ii) the age structure of all saplings between 0.2 and 10 m in height in a 30-year-old gap. In both species, most natural regeneration was clumped and located at the margin of the gaps; however, fir saplings were more represented in understorey environments and less represented in gaps as compared with spruce. Age structure of natural regeneration in the selected gap revealed that the majority (75%) of saplings appeared after the formation of the gap; however, for regeneration taller than 2 m (which has a better chance of reaching the uppermost canopy layer), saplings already present at gap formation predominated. We conclude that (i) gap edges represent a preferential regeneration niche in this forest and (ii) saplings established before gap formation can play an important role in gap refilling. These results provide useful information to ensure, through silvicultural practices, favourable conditions for the temporal and spatial continuity of the regeneration process.

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 score0.837
Threshold uncertainty score1.000

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 head0.030
GPT teacher head0.249
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

Citations74
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Ecology and Biodiversity StudiesFrench-language works237,207