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Record W998360745

Cooperative information systems : a tool for supporting Alberta’s Land-use Framework

2010· article· en· W998360745 on OpenAlexaboutno aff
Ken Banister

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation systemLand useEnvironmental resource managementEnvironmental planningComputer scienceGeographyEnvironmental scienceEngineeringCivil engineering
DOInot available

Abstract

fetched live from OpenAlex

Access to land has become a limiting factor for developments in Alberta. This has led the Government of Alberta to introduce the Land-use Framework (Alberta Sustainable Resource Development, 2008) as a way to improve land-use decision making. Cumulative effects management and the establishment of information and knowledge systems are key components of the framework. With the use of cooperative information systems to combine ecological databases, individual environmental site assessments can be used to build local and regional environmental assessments. This allows for the identification of broader issues that can be missed in individual assessments. As well, local and regional environmental assessments can be developed and reviewed in a timely and effective manner, and government, resource industries, and other stakeholders can be provided with more comprehensive information for decision-making.

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)0.019
metaresearch head score (Gemma)0.028
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.514
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.014
Science and technology studies0.0040.002
Scholarly communication0.0110.009
Open science0.0040.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0260.008

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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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