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Record W7098364575

Session: Urban Development and Growth Patterns

2012· article· en· W7098364575 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsnot available
Fundersnot available
KeywordsLand useThematic MapperArable landUrban planningSustainabilityRationalization (economics)Thematic mapUrban sprawl
DOInot available

Abstract

fetched live from OpenAlex

While Canada ranks second in the world in terms of national land area, its most productive arable land is limited in extent. These lands are also under pressure due to rapid urbanization, especially in the high growth areas of southern Ontario, the Calgary-Edmonton corridor and the lower Fraser River valley of British Columbia. Currently, a program is underway in the Earth Sciences Sector of Natural Resources Canada to quantify urban transportation sustainability in support of energy policy-makers. This work includes the creation of the Canadian Urban Land Use Survey (CUrLUS), a series of land-cover/land-use (LCLU) maps, derived in part from Landsat Thematic Mapper imagery, for all Canadian cities with populations in excess of 200,000. These LCLU maps have potential application beyond transportation issues. To study land conversion impacts during the period 1966-2001, it has been necessary to assimilate this information with historic land use sources from other federal initiatives including the Canada Land Use Mapping (CLUMP) program and the Canada Land Inventory (CLI). This paper addresses assimilation issues through an assessment of the consistency of these information sources leading to a rationalization of their class legends and spatial resolution differences. _________________________ 1.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.197
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 teacher head, not a consensus.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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