MétaCan
Menu
Back to cohort
Record W3136282081 · doi:10.14430/arctic72137

Hydrological Analysis of Municipal Source Water Availability in the Canadian Arctic Territory of Nunavut

2021· article· en· W3136282081 on OpenAlexvenueaboutno aff
Jenny L. Hayward, Lindsay Johnston, Amy Jackson, Rob Jamieson

Bibliographic record

VenueARCTIC · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceClimate changeVulnerability (computing)WatershedWater supplyVulnerability assessmentArcticThreatened speciesGeographyWater securityWater resource managementPopulationWater resourcesEnvironmental resource managementEcologyEnvironmental engineeringHabitatEnvironmental healthPsychological resilience

Abstract

fetched live from OpenAlex

In the Canadian Arctic, the availability of sustainable drinking water supplies is threatened by pressures such as increasing populations, climate change, and the remote geographic nature of the communities. The objective of this study was to conduct a screening level vulnerability assessment of municipal drinking water supplies in the Canadian territory of Nunavut with consideration for climate change, population growth, and infrastructure changes. A hydrological analysis of primary drinking water supply watersheds was performed to evaluate the relative vulnerability level in 24 Nunavut communities. We used a water balance model to predict annual water yield from each watershed using historical and projected future climate data. Approximately 25% of the study communities were projected to experience high vulnerability to water shortages by 2070, defined as using greater than 40% of available water from their source watershed on an annual basis. A medium level of vulnerability (using 20% – 40% of annual available water) was determined for 8% of the study communities and a moderate level for 12% (using 10% – 20% of annual available water). A low vulnerability level to 2070 (using less than 10% of annual available water) was determined for 55% of the communities. The vulnerability level was primarily influenced by source watershed size. The results of this study could be used as a component of a proactive strategy to help address water security issues in Nunavut.

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)0.001
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 score0.141
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.334
Teacher spread0.292 · 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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueARCTICSame topicIndigenous Studies and EcologyFrench-language works237,207