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Record W2011401741 · doi:10.1080/10549811.2014.973611

Introduction to Fire Ecology of the Northeast: Restoring Native and Cultural Ecosystems

2015· article· en· W2011401741 on OpenAlexaboutno aff
Kevin M. Robertson, Helen M. Poulos, Ann E. Camp, Mary L. Tyrrell

Bibliographic record

VenueJournal of Sustainable Forestry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsHavenContext (archaeology)Fire ecologyEcologyGovernment (linguistics)GeographyEnvironmental resource managementLand useRestoration ecologyEcosystemArchaeologyEnvironmental science

Abstract

fetched live from OpenAlex

This special issue is composed of papers presented at a conference hosted jointly by the Yale University School of Forestry and Environmental Studies (New Haven, Connecticut) and Tall Timbers Research Station and Land Conservancy (Tallahassee, Florida) to address the history, ecology, and current need for prescribed fire in native and cultural ecosystems in the Northeast region of the United States and eastern Canada. The conference was held at the Yale University campus on February 20–22, 2014 and involved participants from universities, government agencies, nongovernment organizations, and private business. This special issue presents evidence that the fire history and ecology of the Northeast are strongly context-dependent and result from complex interactions of climate, human land use, and physiography, that certain species within the region are truly fire-adapted, and that synergies between fire management for ecological restoration and public welfare can and should be pursued on the modern landscape. A summary of all conference presentations and syntheses of panel discussions has been published as a Yale Forest Forum Review.

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)0.001
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 score0.127
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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