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Record W4283371444 · doi:10.31223/x5xm00

Groundwater resource allocation in British Columbia: challenges and ways forward

2022· preprint· en· W4283371444 on OpenAlexaffabout
Tom Gleeson, D. M. Allen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
Fundersnot available
KeywordsLivelihoodGroundwaterGroundwater rechargeSustainabilityAquiferAdaptive managementWater resourcesWater resource managementEnvironmental planningResource (disambiguation)Groundwater flowEnvironmental resource managementBusinessEnvironmental scienceNatural resource economicsGeographyEcologyAgricultureComputer scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

Groundwater allocation in British Columbia is facing a number of important challenges as groundwater is licensed under the Water Sustainability Act and potentially included in modern treaties. These challenges include acknowledging the importance of groundwater in supporting environmental flow needs and human water use, the uncertainty and irrelevance of annual recharge estimates, and the under-appreciated importance of aquifer drainage, while tackling cumulative impacts in watersheds using adaptive management with clear sustainability goals. We summarize these challenges and then suggest ways forward so that we can more robustly, holistically and sustainably allocate groundwater resources. Some of the proposed ways forward are based on sound science and are already being implemented partially or in some regions. Not implementing these ways forward risks permanent over-allocation of groundwater resources that would impact stream ecology, endanger rural livelihoods and challenge reconciliation with First Nations.

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)0.002
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0060.003
Scholarly communication0.0080.003
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.210
Teacher spread0.190 · 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
Published2022
Admission routes2
Has abstractyes

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