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Record W2328024213 · doi:10.5849/njaf.12-002

Comparative Assessment of Natural Regeneration Quality in Two Northern Hardwood Stands

2013· article· en· W2328024213 on OpenAlexaboutno aff
Ulrike Hagemann, Sven Wagner

Bibliographic record

VenueNorthern Journal of Applied Forestry · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsYellow birchBeechHardwoodMapleForestrySilvicultureContext (archaeology)Natural regenerationEnvironmental scienceEcologyGeographyBiologyArchaeology

Abstract

fetched live from OpenAlex

Selected quality aspects of natural regeneration in gaps were studied in two sugar maple-yellow birch forest stands in Quebec: a selection-cut stand (SC) and a protected old-growth stand (OG). The quality-assessment systems by Sonderman (1979) and Börner et al. (2003) were applied to saplings and poles of sugar maple (Acer saccharum Marsh.), yellow birch (Betula alleghaniensis Britton), and American beech (Fagus grandifolia Ehrh.) between 2.5‐13.0 m in height to assess stem deviation, forks, the number of live branches, branch diameters, and overall quality. These individual quality parameters, the Sonderman-quality index (QI), and a modified quality index (mQI) were used to illustrate differences in sapling quality between species and stands. Saplings and poles in the OG had fewer forks and fewer live branches of smaller relative branch diameters, resulting in better overall quality compared to the SC. Gap size had no significant influence on individual quality parameters, Sonderman-QI, or mQI class. The combination of both assessment systems into the mQI class allows for a comprehensive evaluation of regeneration quality, which may be used to evaluate the success of silvicultural measures in the context of quality-oriented northern hardwoods management.

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 score0.059
Threshold uncertainty score0.916

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)0.0010.000

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 head0.014
GPT teacher head0.295
Teacher spread0.281 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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