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Record W7061334220

Prescribed fire and design: two biocultural design case studies from northwestern Ontario

2018· dissertation· en· W7061334220 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsResearch designProcess (computing)Descriptive researchHuman factors and ergonomicsQualitative researchFire safety
DOInot available

Abstract

fetched live from OpenAlex

This thesis presents a review of two prescribed burns undertaken in northwestern Ontario. The purpose of this research was to explore the design elements that influenced the prescribed burns utilizing a biocultural design. Biocultural design is a framework for exploring and designing cultural landscapes by considering descriptive and evaluative design elements. Design elements were used to guide the semi-structured interviews and organize the data. Interviews were conducted with community members and fire personnel. The key findings of this research are that there are cultural areas and practices that should be included in future prescribed burn designs. However, there are operational limitations and biological and cultural factors that limit the feasibility of the prescribed burn’s outcomes. The conclusion of this research is that future prescribed burns will benefit from a more intentional designing process that includes cultural elements from the community along with biological and operational factors.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.736

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0190.006
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.255
Teacher spread0.215 · 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 designQualitative
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

Citations0
Published2018
Admission routes2
Has abstractyes

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