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Record W2154108675 · doi:10.1139/x04-157

Postfire environmental conditions influence the spatial pattern of regeneration for <i>Pinus ponderosa</i>

2005· article· en· W2154108675 on OpenAlexvenueno aff
Véronique Bonnet, Anna W. Schoettle, Wayne D. Shepperd

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersRocky Mountain Research Station
KeywordsUnderstorySeedlingQuadratRegeneration (biology)Environmental scienceTransectCanopyLitterForestrySpatial ecologyEcologyGeographyAgronomyBiology

Abstract

fetched live from OpenAlex

Regeneration of ponderosa pine after fire depends on the patterns of seed availability and the environmental conditions that define safe sites for seedling establishment. A transect approach was applied in 2002 to determine the spatial distribution of regeneration from unburned to burned areas within the landscape impacted by the Jasper Fire of 2000 in the Black Hills of South Dakota (USA). Canopy conditions alone, reflecting seed availability, at the stand level were not correlated with regeneration success. However, canopy conditions in combination with ground conditions explained patterns of regeneration success at the plot level (2 m × 6 m scale), and ground conditions explained these patterns at the quadrat level (0.2 m × 0.2 m scale). Only at the finer level of the quadrat could environmental factors explain seedling survival. Safe sites were characterized, in part, by the presence of scorched needle litter on blackened mineral soil. Areas with high understory cover restricted regeneration in the undisturbed forest and reduced seedling survival in the burned areas. The description of environmental conditions that favor and discourage ponderosa pine regeneration success will improve our understanding of how environmental heterogeneity within burned areas will contribute to the future forested landscape.

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 score0.395
Threshold uncertainty score0.969

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 studies0.0000.001
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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

Citations126
Published2005
Admission routes1
Has abstractyes

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