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Record W2035531716 · doi:10.1142/s1464333212500032

ASSESSING LAND SUITABILITY FOR RESIDENTIAL DEVELOPMENT IN PERMAFROST REGIONS: A MULTI-CRITERIA APPROACH TO LAND-USE PLANNING IN NORTHERN QUEBEC, CANADA

2012· article· en· W2035531716 on OpenAlexafffundabout
Katerine Grandmont, Jeffrey A. Cardille, Daniel Fortier, TANIA GIBÉRYEN

Bibliographic record

VenueJournal of Environmental Assessment Policy and Management · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité de MontréalUniversité LavalCenter for Northern Studies
FundersNatural Resources CanadaUniversité de Montréal
KeywordsPermafrostVulnerability (computing)Climate changeLand useGeographyEnvironmental resource managementEnvironmental planningSettlement (finance)Work (physics)PopulationPopulation growthLand-use planningIntersection (aeronautics)Environmental scienceEnvironmental protectionCivil engineeringCartographyEngineeringComputer scienceEcology

Abstract

fetched live from OpenAlex

Northern Quebec (Nunavik) presents an important intersection between population growth and climate change. The Inuit population of Nunavik has the fastest growth rate in any region of Canada. Land-use planning is an urgent and pressing need for northern communities built on permafrost, where there are considerable risks to development in areas where permafrost may thaw. As northern communities work to adapt to climate changes, they will be in great need of confident recommendations about locations of future development projects. This paper presents a case-study of the community of Tasiujaq and assesses the probability of thaw settlement of the surface, a process seriously affecting infrastructure stability. A method is developed for quantifying uncertainty in the resulting map, expressed as a function of judgmentbased uncertainty in the various factors that can influence eventual map quality. The best estimate of vulnerability and of the confidence in that estimate can be expressed in a single, simple map that allows an analyst to convey both of these vital aspects of the assessment process.

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 score0.684
Threshold uncertainty score0.776

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 communication0.0000.001
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 head0.061
GPT teacher head0.309
Teacher spread0.248 · 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

Citations8
Published2012
Admission routes3
Has abstractyes

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