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Record W2260896793

Bioregional Design for Multiple Aggregate Extraction Site Rehabilitation in the Oak Ridges Moraine, Ontario

2015· dissertation· en· W2260896793 on OpenAlexaboutno aff
Heather Schibli

Bibliographic record

VenueThe Atrium (University of Guelph) · 2015
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInvertebrate Taxonomy and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsMoraineAggregate (composite)Extraction (chemistry)GeographyPhysical geographyMaterials scienceChemistry
DOInot available

Abstract

fetched live from OpenAlex

A collaborative project between industry, environmental groups, and government resulted in a bioregional plan for the rehabilitation of multiple aggregate sites in the Township of Uxbridge, Ontario. The research aims were to extract the industry’s best practices from the grey literature and to apply these practices, along with the bioregional plan goals, to conceptual landscape designs that were then used as an engagement tool for landscape stakeholders. The best practices, the bioregional plan goals, and inventory and analysis of the landscape were synthesized to develop alternative landscape scenarios. Stakeholders reviewed and discussed these scenarios, revealing different values and perspectives. Discussion and assessment lead to increased common ground and continued development of bioregional landscape solutions. This process can be a model for rehabilitation planning in other aggregate-rich municipalities.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.904

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.038
GPT teacher head0.226
Teacher spread0.187 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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