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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 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)0.009
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.013
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
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; 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 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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