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Record W4387580110 · doi:10.2166/nh.2023.032

Alternative method for determining available winter water volumes from lakes to support small-scale projects

2023· article· en· W4387580110 on OpenAlexaffabout
Rick Walbourne, Sarah Elsasser, Neil Hutchinson

Bibliographic record

VenueHydrology research · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsHutchinson (Canada)ASL Environmental Sciences (Canada)Government of Northwest Territories
Fundersnot available
KeywordsEnvironmental scienceBathymetryVolume (thermodynamics)Scale (ratio)Hydrology (agriculture)Surface waterEnvironmental resource managementWater resource managementOceanographyEnvironmental engineeringGeologyGeography

Abstract

fetched live from OpenAlex

Abstract In Canada's Northwest Territories (NT), industrial activities conducted during the winter, such as ice road construction and exploratory drilling, require the use of water from ice-covered water bodies. Withdrawal in excess of 10% of available under-ice volume can threaten fish habitat or other users. The Land and Water Boards (LWBs) of the Mackenzie Valley require water licences for water withdrawal beyond regulated thresholds. Applicants must provide information including identification and location of proposed water sources, timing and proposed volume of water and winter water withdrawal must be limited to <10% of available volume to protect fish habitat under the ice. Many applicants are at early project stages and the necessary information on bathymetry and volumes of water is not readily available or requires expertise and effort that may not be feasible at the early stages of smaller projects. This paper describes the alternative method for determining available winter water volumes from lakes to support small-scale projects. A simple formula of ‘allowable volume (m3) = surface area (m2) * 0.1 m’ was developed and tested to provide a conservative estimate of under-ice volumes from easily available data which is protective in spite of uncertainties inherent in limited data.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0200.013

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 head0.234
GPT teacher head0.391
Teacher spread0.158 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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