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Record W2465170904 · doi:10.3934/environsci.2016.3.347

Utilization of bathymetry data to examine lead sediment contamination distributions in Lake Ontario

2016· article· en· W2465170904 on OpenAlexaffabout
K. Wayne Forsythe, Chris Marvin, Danielle E. Mitchell, Joseph Aversa, Stephen Swales, Debbie Burniston, James Watt, Daniel Jakubek, Meghan H. McHenry, Richard Ross Shaker

Bibliographic record

VenueAIMS environmental science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsEnvironment and Climate Change CanadaToronto Metropolitan University
Fundersnot available
KeywordsBathymetryKrigingSillHydrology (agriculture)SedimentGeologyRidgeSpatial distributionEnvironmental sciencePhysical geographyGeomorphologyOceanographyRemote sensingGeography

Abstract

fetched live from OpenAlex

Bathymetry data offer interesting opportunities for the analysis of contaminant distribution patterns. This research utilized lead surficial sediment sample data from Lake Ontario that were collected by the Canada Centre for Inland Waters in 1968 and 1998. Traditionally, two-dimensional analyses such as dot maps or proportional circle representation have been utilized to examine pollutant levels. Generating area estimates allows for expanded spatial analysis of contaminant distribution patterns. Lake-wide surfaces were derived using the ordinary kriging technique. These were then layered on bathymetry data to examine three-dimensional relationships between observed pollution patterns and lake-bottom features. Spatial variability was observed in both the 1968 and 1998 datasets. Contamination levels in 1998 dropped substantially, especially in areas that were previously the most heavily polluted and above the Probable Effect Level (4660.23 km2 or 26.72% of the common analysis area lake-bottom in 1998 versus 6189.07 km2 or 62.00% in 1968). Conversely, areas below the Threshold Effect Level increased from 922.09 km2 (5.29%) in 1968 to 3484.22 km2 (19.98%) in 1998. In both years, shallow and sill/ridge areas tended to have lower levels of contamination than deeper lake basins or contaminant inflow areas. The 1968 dataset likely provides a more detailed estimation surface as there were more points available for interpolation procedures. The kriging surfaces when combined with bathymetry, sedimentology information, and knowledge of physical processes provide a comprehensive illustration of the contaminant distributions whether they are high (1968) or when loadings are significantly reduced (1998). The results have implications for future sediment assessment programs and survey design on a lake-wide basis. The bathymetry data allowed for enhanced interpretation and an improved understanding of observed lead pollution patterns.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.271
Teacher spread0.227 · 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 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

Citations8
Published2016
Admission routes2
Has abstractyes

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