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Record W4384696498 · doi:10.22215/etd/2023-15507

Impressions of the Natural Environment: Balancing Environmental Protection through Human Engagement in Rouge National Urban Park

2023· dissertation· en· W4384696498 on OpenAlexaboutno aff
Margaret Louise Combaluzier

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsNational parkRecreationGeographyVisitor patternUrban sprawlTourismEnvironmental planningEnvironmental resource managementEnvironmental protectionUrban planningEcologyArchaeologyEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Nestled within suburban sprawl and farmland in the Greater Toronto Area, Rouge National Urban Park is the amalgamation of rich Carolinian forest ecology, urban pressures, and vital Rouge River networks. An important site for recreation, the park is easily accessible by over 20% of the Canadian population. The park has recently undergone a period of management change and increased tourism.Unprecedented within the larger national park network, the park presents a unique opportunity for human engagement, habitat conservation and future development. Based on research, site visits, material studies and mapping, this thesis revises the Rouge National Urban Park Management Plan to reflect site specificity and programmatic needs. This thesis proposes a series of design interventions that balance human influence and environmental conservation by reinterpreting architectural elements of a typical visitor centre and by exploring what it means to visit a place of ecological significance.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.891
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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