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Record W7052925469

Stepping lightly: a rural site approach for habitat restoration

2022· dissertation· en· W7052925469 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2022
Typedissertation
Languageen
FieldEngineering
TopicParticle Accelerators and Free-Electron Lasers
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatDeforestation (computer science)LoggingWildlife corridorAgricultureRestoration ecologyDeciduousNatural (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

In light of the global decline of biodiversity, this thesis seeks to
\naddress local issues threatening species which could lead to greater
\npositive global impact. The local issues experienced within the Lake
\nErie-Lake Ontario Ecoregion, located in Southwestern Ontario, reveal
\nhabitat loss as the key threat to biodiversity. The majority of habitat was
\nlost as a result of deforestation caused by the logging industry and the
\nclearing of the landscape for agricultural use. Forests were once allencompassing entities covering Southwestern Ontario, today they sit at
\nthe margins of agricultural space. To address habitat loss, this thesis
\nargues a design approach to connect the existing fragmented habitat
\nis required. These connections are established through ecocorridors,
\nnatural spaces which link existing habitats. A specific site in the rural
\nland to the west of London, Ontario is selected to develop a strategy for
\nreforesting. A site approach developed from the natural forest succession
\nof deciduous forests is established to guide the growth of corridors
\nand architectural interventions. A forest center as well as a number of
\nsmall-scale interventions seek to support the programmatic needs of
\nreforesting while also integrating themselves into the forest, acting as
\nsupports for the wildlife. This thesis seeks to develop a site strategy
\nsupported by an architectural intervention which connects fragmented
\nhabitat and di

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.196
Teacher spread0.188 · 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.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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