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Record W2914644215 · doi:10.4224/23004810

Geospatial data for developing nutrient SPARROW models for the Midcontinental region of Canada and the United States

2018· article· en· W2914644215 on OpenAlexafffundvenueabout
Ivana Vouk, Richard S. Burcher, Craig Johnston, R. Wayne Jenkinson, David A. Saad, John S. Gaiot, Glenn Benoy, Dale M. Robertson, Michael T. Laitta

Bibliographic record

VenueNPARC · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsMinistry of Natural Resources and ForestryNational Research Council CanadaInstitut National de la Recherche Scientifique
FundersU.S. Geological SurveyMinistry of EnvironmentEnvironment and Climate Change CanadaAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural AffairsNational Research Council CanadaMinistry of Environment - Saskatchewan
KeywordsGeospatial analysisSparrowGeographyNutrientData scienceRemote sensingEcologyBiologyComputer science

Abstract

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Through the International Watersheds Initiative of the International Joint Commission (IJC), the Spatially-Referenced Regressions on Watershed attributes (SPARROW) model developed by the U.S. Geological Survey (USGS) is being applied to the Great Lakes, Rainy River – Lake of the Woods and Red-Assiniboine basins. The objective of this binational application of the SPARROW model is to better understand and quantify the sources of phosphorus (P) and nitrogen (N) that contribute to regional water quality issues like algal blooms and eutrophication in Lake Erie and other parts of the Great Lakes, as well as Lake of the Woods. Led by the IJC, a team of researchers from the National Research Council of Canada – Ocean, Coastal and River Engineering Research Centre, USGS, and IJC are extending the SPARROW modelling work previously completed for the Red-Assiniboine basin and the U.S. portions of the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy river basins to cover all of the Great Lakes, Rainy River – Lake of the Woods and Red-Assiniboine basins. The current effort is termed the Midcontinent SPARROW modelling study. This report describes the data used to develop the Midcontinent SPARROW models, specifically the sources of original data, assembling the data, and the processing and harmonization required between the U.S. and Canada data needed to produce these models. Details provided include the: • development of a digital stream network and related catchments – most significantly in the Canadian regions of the Great Lakes and Rainy River – Lake of the Woods basins where these data were not available to create a seamless binational network across the model domain; • calculation of variables to aid in the determination of in-stream and in-reservoir decay of P and N; • quantification of Canadian diversions within the Midcontinental region (i.e., Lake St. Joseph, Long Lake and the Ogoki Reservoir); • development of binational input nutrient sources considered for model development (i.e., land cover, inorganic farm fertilizer, manure, atmospheric deposition, point source pollution from wastewater treatment plants and contribution from non-modelled watersheds); and • development of delivery variables considered to be most predominant (i.e., temperature, precipitation and ensuing runoff, soil permeability and clay content, slope of the catchments, and tile drainage). The majority of the geospatial data collection and processing was required for Canadian datasets because many of the U.S. datasets were already assembled for previous SPARROW model applications in the U.S. The task of harmonizing data between the U.S. and Canada was important to ensure consistency of the datasets used in the models. The harmonized digital stream network, delineated catchments and input data for each catchment (i.e., source and delivery variables), created for the Midcontinent SPARROW models, are available for download at url https://doi.org/10.4224/300.0001

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.738

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)0.0000.000

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 head0.032
GPT teacher head0.214
Teacher spread0.181 · 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 designSimulation or modeling
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

Citations4
Published2018
Admission routes4
Has abstractyes

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