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Record W6892285661 · doi:10.5066/p947b9fi

Lake Erie Ecological Investigations 1980-2000

2018· dataset· en· W6892285661 on OpenAlexaboutno aff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsShoreBenthic zoneFish <Actinopterygii>Fish killPollutionWater pollutionGeological surveyAquatic ecosystemSediment

Abstract

fetched live from OpenAlex

The Lake Erie Ecological Investigations (LEEI) dataset, housed at the Great Lakes Science Center (GLSC) was developed during a reevaluation of Lake Erie Areas of Concern (AOCs) from 1998-2000. These AOCs were recognized as such by the International Joint Commission (IJC) of the United States and Canada due to their severe water pollution problems. The dataset includes data from both the 1998-2000 reevaluation as well as data from other historical evaluations from the 1980's to mid-1990's for comparison where available. Data Description: Rivers and harbors of the Great Lakes have been impacted for decades by heavy industrialization, densely populated areas, and agriculture resulting in contamination, eutrophication, and physical degradation of these ecological areas. Focusing on designated AOCs, such as the Detroit, Cuyahoga, and Black Rivers in Michigan and Ohio, USGS scientists and collaborators gathered data in these evaluations to provide evidence of effects on aquatic biota. Beginning in the 1980's, researchers set out to find cause-effect linkages between observed external and internal abnormalities in fish and contaminants in designated AOCs. The suspected cause of these abnormalities was sediment contamination from organic chemicals and pesticides discharged by industries along the Lake Erie shore and its tributaries. Data from the 1980's were mostly focused on three Ohio sites: two designated AOC sites, the Cuyahoga River (Cleveland, OH) and the Black River (Lorain, OH); and one reference site, the Huron River (Huron, OH), as studied by Stephen B. Smith et al. in 1986-87 (Smith et al. 1994). Sediments, benthic invertebrate, and fish related data were collected from all three sites. Sediments were tested for concentrations of heavy metals, pesticides, and polyaromatic hydrocarbons (PAHs), as well as particle size and moisture. Population and diversity estimates were calculated for fish, insects, and other bottom dwelling invertebrates to address the hypothesis that chemicals affect the health of aquatic biota living at these sites. Brown bullhead fish were evaluated for growth estimate and rates of liver tumors and other abnormalities. In 1998-2000, the LEEI initiated a reevaluation of the historic Lake Erie collections, but on a much larger scale---at nine AOC sites and two reference sites---in order to evaluate changes in the water quality. The sites included the Ashtabula River, OH; Black River, OH upstream; Buffalo River, NY; Cleveland Harbor, OH; Cuyahoga River, OH upstream; Detroit River, MI; Huron River, OH (reference); Niagara River, NY; Old Woman Creek, OH (reference); Ottawa River, OH; and Presque Isle Bay, PA. This dataset again includes sediment, benthic invertebrate, and fish related data. Sediments were collected for organochlorine (OCs), PAHs, total organic carbon (TOC), inorganic trace elements, particle size, and moisture analysis. Fish and invertebrate communities were evaluated for relative abundance and diversity. Brown bullhead were collected for histopathological evaluation and reproductive biomarker alterations possibly caused by exposure to endocrine-disrupting chemicals such as pesticides and polychlorinated biphenyls (PCBs). In-field external and internal health examinations of primarily brown bullhead were conducted and samples were gathered when needed for further laboratory analysis. Data Structure: For the purposes of delivery, the dataset has been divided into four conceptual groups according to what was evaluated by LEEI: Fish Community Analysis, Fish Health Evaluation of primarily brown bullhead, Benthic Invertebrate Community Analysis, and Sediment Analysis. Additionally, there is a Reference Tables group, with reference tables that apply to multiple conceptual groups. Each conceptual subgroup focuses on the data from the 1998-2000 reevaluation, and also contains data from earlier historical studies for comparison. The historical data for the Fish Community Analysis, Invertebrate Community Analysis, and Sediments subgroups, are from the study by Stephen B. Smith, Marc A. Blouin, and Michael J. Mac (Smith et al. 1994) on the fish populations, benthic invertebrates, and sediments in the Cuyahoga, Black, and Huron Rivers in Ohio. The historical data for the Fish Health subgroup includes data from the study by Stephen B. Smith et al. as well as data from other studies; these historical studies are described in more detail in the supplemental information section of this metadata and in Attachment 1 of the attached Quality Assurance Project Plan (QAPP). Data Quality: This data release includes all pertinent data from the dataset, but not all data tables from the Oracle database at GLSC were included. Some data tables were not released as they are believed, by the metadata creator, to contain duplicate information to what is included in this release. In some cases, tables containing data from historical studies were not released as no supporting documentation exists at GLSC to understand and document what data are in these tables. Original data tables remain saved unchanged in Oracle at GLSC; however, certain changes were made to the csv files extracted for release for ease of use and understanding of the data upon publishing (see Process Steps for more details). As this data set primarily contains data from the 1998-2000 revaluation of Lake Erie AOCs, but also contains data from historical studies, users of this data release should be aware that different studies had different standards of data collection and quality checking. The metadata creator has noted where possible fields and codes that may have changed for different studies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.062
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0480.110

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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