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Record W2325045388 · doi:10.1139/er-2015-0070

Perceptions of bushfire risk mitigation and biodiversity conservation: a systematic review of fifteen years of research

2016· review· en· W2325045388 on OpenAlexvenueno aff
Emily Moskwa, Inkeri Ahonen, Ville Santala, Delene Weber, Guy M. Robinson, Douglas K. Bardsley

Bibliographic record

VenueEnvironmental Reviews · 2016
Typereview
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityContext (archaeology)Environmental resource managementEnvironmental planningBiodiversity conservationRisk managementPerceptionGeographyBusinessEcologyEnvironmental sciencePsychologyBiology

Abstract

fetched live from OpenAlex

Bushfire management systems can potentially undermine conservation policy if people do not value biodiversity conservation or understand what constitutes effective fire management. Our objective for this study was to review recent social research that explores public and practitioner perceptions of risk mitigation and biodiversity values in relation to bushfire management. To do this we undertook a systematic review of bushfire management literature published over a 15-year period from the year 2000 to 2014 to evaluate the current state of knowledge addressing public and practitioner perceptions of the relationship between bushfire risk and biodiversity conservation within a fire management context. A total of 39 articles addressed this issue, suggesting a disconnect between research into perceptions of bushfire risk mitigation and perceptions of biodiversity conservation. An integrated research approach that addresses the social component of the impact of risk mitigation policy and biodiversity conservation strategies is needed.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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)0.0010.001

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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2016
Admission routes1
Has abstractyes

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