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Record W2065790778 · doi:10.1139/x04-017

Ageing and decline of trembling aspen stands in Quebec

2004· article· en· W2065790778 on OpenAlexvenueaboutno aff
David Pothier, Frédéric Raulier, Martin Riopel

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsThinningStand developmentEnvironmental scienceForest inventoryForestrySilvicultureSite indexMathematicsGeographyForest management

Abstract

fetched live from OpenAlex

The onset of stand decline is a critical stand development characteristic that can affect both harvesting schedule and annual allowable cutting volume. The age at which stand decline begins was assessed in trembling aspen (Populus tremuloides Michx.) stands in the province of Quebec, Canada, by three different methods. The first used a data source consisting of 1081 temporary sample plots from which a segmented regression model was fitted to detect any deviation from the self-thinning line. This type of deviation was observed below densities of 720 stems/ha, which is normally attained around age 60 on good-quality sites. The second used a data source composed of 34 permanent sample plots that allowed us to calculate the age at which the periodic annual increment was equal to zero. The estimated age at which stands began to decline averaged 64 years, but the large variation around this mean was poorly explained by site factors. The third method used a data source consisting of 98 inventory plots. Volume of tree mortality and of wood decay showed an important increase when stand age was around 60. Hence, the results from three independent sources of data converge toward a generalized loss of aspen volume around age 60. However, the prediction of the age at which decline begins in any particular aspen stand is imprecise and requires an on-site measurement of the stand state.

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 score0.309
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.045
GPT teacher head0.324
Teacher spread0.280 · 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

Citations53
Published2004
Admission routes2
Has abstractyes

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