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<scp>M</scp>apping Alberta's surface water resources for the period 1971–2000

2013· article· en· W1914554301 on OpenAlexafffundvenueabout
S. W. Kienzle, Markus Mueller

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Lethbridge
FundersAlberta Water Research Institute
KeywordsWatershedEnvironmental scienceSurface runoffStreamflowHydrology (agriculture)Water resourcesWater qualityPrecipitationSurface waterPeriod (music)Drainage basinWater resource managementGeographyGeologyMeteorologyEnvironmental engineeringCartographyEcology

Abstract

fetched live from OpenAlex

Abstract Although the sustainability and wealth of Alberta is closely linked to the quantity, quality, and management of its water resources, the province lacks watershed‐based inventories of its water supplies. The mean annual water yield for 287 gauged watersheds across Alberta was calculated for the period 1971–2000 by relating available streamflow records with the respective watershed areas. The percent contribution of each watershed relative to the outlet of each of the 16 major river basins located in Alberta was computed, which enabled the identification of the most important water‐producing regions. The integration of high‐resolution precipitation normal maps, available for the same period, enabled the calculation of mean annual runoff coefficients and the determination of mean annual regional water balances. These analyses resulted in important water resources baseline data, against which impacts of environmental and climate change can be compared. All data are available online in the form of spreadsheets and shapefiles.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.015
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.171
Teacher spread0.165 · 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 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

Citations14
Published2013
Admission routes4
Has abstractyes

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