MétaCan
Menu
← Back to cohort
Record W6960133015 · doi:10.11575/prism/30129

Alberta Water Resources, Policies, Legislation and Goals: The Quest to Awaken "Sleeper Rights"

2014· other· en· W6960133015 on OpenAlexfundaboutno aff

Bibliographic record

VenueOpen MIND · 2014
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
FundersAlberta InnovatesGovernment of AlbertaAlberta Water Research Institute
KeywordsNucleofectionWork (physics)TSG101Quality (philosophy)Subpoena

Abstract

fetched live from OpenAlex

Water is arguably the most critical natural resource to Alberta’s future. The quantity and quality of water will shape the social, economic, and environmental dimensions of Alberta’s future. The quality of life in Alberta will depend on our ability to allocate this finite resource in both an efficient and environmentally responsible manner. The issue addressed in this research is how Alberta’s current water policies manage “sleeper rights” and why these policies need to be updated. Sleeper rights describe water licenses that are allocated to a water user but are not fully utilized. This allocated but under-utilized water is important because it helps Alberta’s major watersheds to meet its instream flow needs (IFNs). IFNs refer to the amount of water that aquatic ecosystems require to provide Albertans with safe and secure drinking water; healthy aquatic ecosystems; and reliable quality water supplies. By the end of 2005, the Alberta Environment and Sustainable Resource Development (AESRD) had allocated approximately 9.5 billion cubic metres of water throughout Alberta. By the end of 2010, this had increased to 9.9 billion cubic metres. The three sectors representing the highest water demands and allocations in Alberta are the agricultural sector (44.3%), commercial sector (29.5%), and municipal/ domestic sector (11.3%). However, not all of these allocations are fully utilized. By some estimates, as much as 45 percent of water allocated under license in Alberta remains unused.

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)0.004
metaresearch head score (Gemma)0.005
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: Other
Teacher disagreement score0.079
Threshold uncertainty score0.574

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0100.005
Scholarly communication0.0110.003
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueOpen MIND→Same topicWheat and Barley Genetics and Pathology→French-language works237,207→