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Record W1986998519 · doi:10.3375/043.032.0309

Planning the Far North in Ontario, Canada: An Examination of the “Far North Act, 2010”

2012· article· en· W1986998519 on OpenAlexaboutno aff
Christopher J. A. Wilkinson, Tyler M. Schulz

Bibliographic record

VenueNatural Areas Journal · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicSocioeconomics of Resources and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsConvention on Biological DiversityGeographyGovernment (linguistics)Environmental resource managementEnvironmental protectionEnvironmental planningBiodiversityEcologyEnvironmental science

Abstract

fetched live from OpenAlex

In 2011, the Government of Ontario, Canada, enacted the “Far North Act, 2010” to protect ecological systems and areas of cultural value in northern Ontario in an interconnected network of protected areas. This law establishes that at least 225,000 square kilometres of northern Ontario, known as the Far North, will be protected through the creation of community-based land-use plans. A central purpose of the “Far North Act, 2010” is to create a significant role for First Nation communities in land-use planning, which is cast as a joint responsibility with the Government of Ontario. The maintenance of biological diversity, ecological processes, and ecological functions — including the storage and sequestration of carbon — are key objectives of this land-use planning initiative. This law sets an ambitious target for protected areas coverage; once implemented, terrestrial protected area coverage will cover 26.5% of the Province of Ontario, greatly exceeding the target of 17% coverage for signatories of the international Convention on Biological Diversity by the year 2020.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.234
Threshold uncertainty score0.888

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0120.003
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.029
GPT teacher head0.199
Teacher spread0.169 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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