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Record W2002095151 · doi:10.1139/x03-265

Gap dynamics and replacement patterns in gaps of the northeastern boreal forest of Quebec

2004· article· en· W2002095151 on OpenAlexvenueaboutno aff
A. Pham, Louis De Grandpré, Sylvie Gauthier, Yves Bergeron

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsAbies balsameaBlack spruceBalsamWindthrowTaigaForestryForest dynamicsCanopyGeographyEcologyDisturbance (geology)Yellow birchForest structureEnvironmental scienceBiologyHardwoodBotany

Abstract

fetched live from OpenAlex

The northeastern boreal forest of Quebec is characterized by a humid climate. Consequently, fires are less frequent and small-scale disturbances play an important role in forest dynamics. Natural mortality and nonfire disturbances such as insect outbreaks and windthrow lead to gap-driven processes. Changes in structure and species composition can result from gap dynamics. The objectives of this study were to characterize gaps and examine patterns of species replacement in gaps in old conifer stands. Line intersect sampling was used to sample stands dominated by balsam fir (Abies balsamea (L.) Mill.) and (or) black spruce (Picea mariana (Mill.) BSP). Results show that 54% of the forest was in expanded gaps and that canopy gaps are relatively small, since 87% of them were smaller than 100 m 2 . The majority (94%) of the openings were caused by the mortality of less than 10 gap makers. Replacement probabilities show self-replacement of A. balsamea in Abies stands and of P. mariana in Picea stands. However, in Abies-Picea stands, there seems to be a reciprocal replacement of the two species. These results provide knowledge of the disturbance dynamics of the region as a basis for development of silvicultural practices that preserve the structural components of older forest stands.

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 score0.214
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations155
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207