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Planning for Long, Wide Conservation Corridors on Private Lands in the Oak Ridges Moraine, Ontario, Canada

2007· article· en· W2061708818 on OpenAlexafffundabout
Graham S. Whitelaw, Paul F.J. Eagles

Bibliographic record

VenueConservation Biology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsUniversity of Waterloo
FundersGovernment of Ontario
KeywordsMoraineGeographyForestryNature ConservationLand useAgroforestryArchaeologyPhysical geographyEnvironmental scienceEcologyGlacierBiology

Abstract

fetched live from OpenAlex

We explored the role of conservation biology in the planning of a natural-heritage system that includes long, wide conservation corridors situated primarily on private lands, and established to connect natural core areas in the Oak Ridges Moraine of Ontario, Canada. We based our review on government documents, semi-structured interviews with participants involved in this land-use planning process, and our involvement with the issue from 1990 through 2002. Conservation biology had a major influence on the outcome of the land-use planning process for this moraine. The landform was identified as an area of value by the environmental movement within the context of a number of ongoing government studies that began in the late 1980s and early 1990s. Conservation biologists and planners in government, the environmental movement, and the private sector carried out work related to conservation biology, including inventories and the development and application of criteria for the delineation of core areas and conservation corridors. Once the political timing was favorable (2001-2002), decision makers linked the science of conservation biology to planning policies and law in Ontario. The Oak Ridges Moraine land-use planning process was precedent setting in Canada, and possibly internationally. To our knowledge this is the first time long, wide conservation corridors on private lands were regulated through land-use-planning legislation and led to restrictions on urban development and aggregate resource extraction.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.258
Teacher spread0.238 · 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 designObservational
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

Citations24
Published2007
Admission routes3
Has abstractyes

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