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Record W6921631540 · doi:10.7910/dvn/pnwdrb

Extracted Data From: Lake Level Viewer

2025· dataset· en· W6921631540 on OpenAlexaboutno aff

Bibliographic record

VenueHarvard Dataverse · 2025
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMetadataSeicheGeospatial analysisGeodetic datumWater levelDigital dataNorth American Datum of 1927Data managementHydrology (agriculture)

Abstract

fetched live from OpenAlex

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: NOAA Office for Coastal Management. (2025, March 1). Lake Level Viewer. Data Download. https://chs.coast.noaa.gov/htdata/Inundation/GreatLakes/BulkDownload/index.html. If you have questions about the underlying data stored here, please contact NOAA Office for Coastal Management by submitting this form: https://coast.noaa.gov/contactform/. If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. This dataset consists of the geospatial data layers that are behind NOAA's Digital Coast Lake Level Viewer, which models lake level rise and flooding impacts along the US side of the Great Lakes. The water levels illustrate the extent and relative depth of water from 3 feet below to 10 feet above each lake's respective Low Water Datum (LWD). Water extent is as it would appear on a calm day with no wind-driven waves or seiche effect. The data do not consider natural processes such as erosion, subsidence, or future construction. The data are intended for use as a screening-level tool for planning and management decisions. Datasets are subdivided into the following folders, and in some cases are geographically segmented by portions of the individual lakes (Erie, Huron, Michigan, Ontario, St. Clair, and Superior): DEMs: lidar-based high resolution digital elevation models stored as geotiffs Depth Rasters: rasters of lake level rise inundation from -3 ft to 10ft above the LWD as geotiffs Extent Rasters: single value rasters of lake level rise inundation from -3 ft to 10ft above the LWD as geotiffs Lake Level Vectors: polygon data representing the extent of lake level rise inundation as geopackages Documentation for the LL tool and methodology for creating the datasets is included in a PDF file. https://chs.coast.noaa.gov/htdata/Inundation/GreatLakes/BulkDownload/index.html

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.578
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4220.346

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 head0.122
GPT teacher head0.258
Teacher spread0.136 · 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; the direct Gemma label and the distilled Codex classifier 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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