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Record W1651334091 · doi:10.15273/pnsis.v45i2.3985

Post-hurricane coniferous regeneration in Point Pleasant Park.

2010· article· en· W1651334091 on OpenAlexaffabout
James W.N. Steenberg, Peter N. Duinker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDisturbance (geology)National parkTransectGeographyRegeneration (biology)ForestryEcological successionEcologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Point Pleasant Park in Halifax, Nova Scotia, sustained catastrophic forest disturbance from Hurricane Juan in September, 2003. This study assessed the adequacy of natural coniferous regeneration in the park in the fall/winter of 2006-2007 and compared the regeneration with pre- and post-disturbance park surveys. The park was stratified using existing trails, transects were spaced 10 m apart, and 20 m2 plots were laid every 10 m. There was a large observed variation of seedling density, with the highest densities being found in the northern and western section of the sample area, and the lowest being found in the south-east. Red spruce was the dominant regenerating species. Balsam fir showed a high variation in density. White pine was less dense and fairly uniformly distributed whereas eastern hemlock had a sparse and patchy distribution. White spruce and exotic species were sparse and tended to be found in areas with lower total regeneration. The comparison with the pre- and post-disturbance park surveys revealed regeneration similar in composition to existing and pre-disturbance forest cover. There are several park management techniques that could benefit park recovery such as the use of donor sites, the importation of favourable conifer species for fill-planting, the culling of some exotic species, and volunteer planting programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.473
Threshold uncertainty score0.940

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2010
Admission routes2
Has abstractyes

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