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Record W2303623270 · doi:10.5558/tfc2016-017

Utikuma Region Study Area (URSA) – Part 1: Hydrogeological and ecohydrological studies (HEAD)

2016· article· en· W2303623270 on OpenAlexafffundvenueabout
K. J. Devito, C. A. Mendoza, Richard M. Petrone, J. M. Waddington

Bibliographic record

VenueThe Forestry Chronicle · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster UniversityUniversity of WaterlooUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSyncrude
KeywordsBorealLandformEnvironmental scienceTaigaWatershedHydrogeologyClimate changeHydrology (agriculture)EcohydrologyDisturbance (geology)Physical geographyEcosystemGeologyGeographyGeomorphologyEcologyForestryOceanography

Abstract

fetched live from OpenAlex

The Utikuma Region Study Area (URSA) was initiated to develop spatially explicit modelling tools to predict the cumulative impacts of land use and natural disturbance on the Boreal Plains (BP) ecozone of the Western Boreal Forest. Research comprised several multi-year projects, spanning wet and dry climate periods that combined intensive detailed process studies at seven watersheds with extensive long-term ecohydrological and hydrogeological studies conducted across a 60-km transect representing the range of glaciated landforms characteristic of the sub-humid boreal forest in Alberta. These studies have improved our conceptual understanding and capacity to numerically model how climate and geology influence water and energy flow, and the hydrologic linkages and natural variability of the key processes influencing BP ecosystems. Eco-hydrogeological frameworks have been developed for designing, conducting, interpreting, and extrapolating research results for watershed management and construction across the Boreal Plain ecozone.

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 score0.007
Threshold uncertainty score0.999

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)0.0020.000

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 head0.132
GPT teacher head0.292
Teacher spread0.160 · 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

Citations26
Published2016
Admission routes4
Has abstractyes

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