NOAA Office for Coastal Management Coastal Digital Elevation Model: Lake Superior
Bibliographic record
Abstract
these data were created as part of the national oceanic and atmospheric administration office for coastal management s efforts to create an online mapping viewer called the noaa lake level viewer it depicts potential lake level rise and fall and its associated impacts on the nation s coastal areas the purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at lake level change coastal flooding impacts and exposed lakeshore the viewer is a screening level tool that uses nationally consistent data sets and analyses data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios the noaa lake level viewer may be accessed at https coast noaa gov llv this metadata record describes the lake erie digital elevation model dem which is a part of a series of dems produced for the national oceanic and atmospheric administration office for coastal management s lake level viewer described above this dem includes the best available lidar and us army corps of engineer dredge survey data known to exist at the time of dem creation that met project specifications this dem includes data for monroe and wayne counties in michigan chautauqua and erie counties in new york ashtabula cuyahoga erie lake lorain lucas ottawa sandusky and wood counties in ohio and erie county in pennsylvania the dem was produced from the following lidar data sets 1 2011 2012 usace ncmp topobathy lidar lake erie mi ny oh pa 2 2011 usace ncmp topobathy lidar mi ny great lakes 3 2008 fema lidar erie county ny 4 2007 usace ncmp topobathy lidar lake erie erie county pa and lake michigan manitou islands mi pa 5 2007 usace ncmp topobathy lidar lake erie ny shoreline 6 2006 usace ncmp topobathy lidar lake erie oh pa lake huron mi and lake michigan porter county in 7 2007 pennsylvania department of conservation and natural resources pa dcnr statewide lidar 8 2006 ohio statewide imagery program osip lidar north the dem was produced from the following sonar data sets 9 2015 usace detroit district detroit river mi livingstone channel reach 10 2015 usace buffalo district ashtabula harbor oh 11 2015 usace buffalo district erie harbor pa 12 2015 usace buffalo district fairport harbor oh 13 2015 usace buffalo district rocky river oh 14 2013 usace buffalo district buffalo harbor ny buffalo river and ship canal 15 2014 usace detroit district point mouillee mi 16 2014 usace buffalo district conneaut harbor oh 17 2014 usace buffalo district dunkirk harbor ny 18 2014 usace buffalo district niagara river ny 19 2014 usace buffalo district sandusky harbor oh the dem is referenced vertically to the north american vertical datum of 1988 navd88 with vertical units of meters and horizontally to the north american datum of 1983 nad83 the resolution of the dem is approximately 3 meters
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".