MétaCan
Menu
Back to cohort
Record W3170832655 · doi:10.1061/9780784483466.014

Investigation of Riverine Loading Impacts on the Lower Green Bay Hydrodynamic Regimes

2021· article· en· W3170832655 on OpenAlexaboutno aff
Fuad Bin Nasir, Bahram Khazaei, Héctor R. Bravo

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2021 · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
Fundersnot available
KeywordsBayEnvironmental scienceHydrology (agriculture)GeologyOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

The world’s largest freshwater estuary, Green Bay, was a pristine habitat for centuries. The development of manufacturing industries in the area caused widespread pollution and natural habitat loss that resulted in the designation of the Lower Fox River and the Lower Green Bay system as an area of concern (AOC) by the International Joint Commission of Canada and the United States. The world’s largest polychlorinated biphenyls (PCB) cleanup and habitat restoration projects took place in the Lower Fox River. Lower Green Bay receives flows and sediment and nutrient loadings from five major tributaries: Fox River, Menominee River, Oconto River, Peshtigo River, and Duck Creek. Previous studies showed that most nutrient inputs to Green Bay are delivered by the Fox River—estimated to be approximately one-third of the total nutrient loading to Lake Michigan. The authors previously investigated loading impacts from the two largest tributaries to the bay, namely the Fox and Menominee Rivers, using the Finite-Volume Community Ocean Model (FVCOM), an unstructured-grid, free-surface, three-dimensional circulation model. This study investigates the impacts of the inclusion of the additional tributaries Oconto River, Peshtigo River, and Duck Creek on the Green Bay circulation and thermal regimes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.992

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.007
GPT teacher head0.177
Teacher spread0.171 · 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.

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueWorld Environmental and Water Resources Congress 2021Same topicHydrology and Sediment Transport ProcessesFrench-language works237,207