MétaCan
Menu
Back to cohort
Record W4362675034 · doi:10.1016/j.ejrh.2023.101377

Assessing and predicting Lake Chloride Concentrations in the Lake-Rich Urbanizing Halifax Region, Canada

2023· article· en· W4362675034 on OpenAlexafffundabout
Tessa Bermarija, Lindsay Johnston, Christopher S. Greene, Barret L. Kurylyk, Rob Jamieson

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWatershedEnvironmental scienceStormwaterHydrology (agriculture)Nova scotiaGeographyEcologySurface runoffArchaeologyGeology

Abstract

fetched live from OpenAlex

Halifax, Nova Scotia, Canada. Many lakes in the Halifax region are approaching or exceeding the chronic freshwater aquatic life guideline for chloride (Cl-), presumably due to the application of deicing salts. These exceedances represent an ecological risk that requires mitigation for lakes currently experiencing high Cl-, and preventative steps should be taken to protect lakes that will be impacted by future development. In this study we paired geospatial analysis with linear regression methods to identify key factors contributing to elevated Cl- in Halifax lakes, and applied a mass balance modeling approach to estimate annual Cl- loading rates for dominant land uses within the region. The watershed variables found to be most predictive of mean lake [Cl-] were the % urban coverage, road density, and stormwater pipe density in a watershed. Annual Cl- loading rates for the four primary land use categories in the region were: rural 1–3 g/m2/yr, commercial 0–9 g/m2/yr, residential 97–162 g/m2/yr, and roads 804–964 g/m2/yr. The mass balance model developed in this study could be used to predict Cl- loading associated with planned developments, and subsequent impacts on receiving lakes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.783

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.271
Teacher spread0.232 · 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.

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

Citations5
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Hydrology Regional StudiesSame topicSmart Materials for ConstructionFrench-language works237,207