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Record W7064735142

Building bridges in the backcountry: a case study of design in the headwaters region of the Oldman watershed

2019· dissertation· en· W7064735142 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)Process (computing)WatershedAction researchAction (physics)Design processEngineering design processKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

Increasing pressures on the Eastern Slopes in Southern Alberta are leading to escalated tensions and debates of sustainable public land use, access, and management for the region. This research project was a case study of the bridge building design project initiated by the Crowsnest Pass Quad Squad. The purpose of this research was to investigate the bridge building design process to discover applications that can be taken from this practical design approach to the concept of biocultural design. Opportunities for learning and changed perspectives were also observed through participant observation, interviews, and design workshops as the ATV users seek to mitigate their own impact. Research findings suggest the importance of a biocultural design team and explicit guiding coordinates, or key values, that will guide the design process the team implements. The bridge building program provides lessons that can be considered in the practice of biocultural design; namely, innovations have led to stronger and more efficient bridges, opportunities for collaboration and participation have yielded platforms for learning to occur, and collective social action has taken place as participants seek to mitigate their impact within the Eastern Slopes.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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 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
Published2019
Admission routes1
Has abstractyes

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