Bibliographic record
Abstract
Due to an accelerated decline in water quality, Florida Bay is the focus of an inter-agency restoration program involving a modeling effort to define water circulation patterns both internally and between its surrounding waters. Models such as these require adequate resolution of the Bay's morphologic features which are characterized by extensive shallow water networks of mud banks, cuts, and basins. However, the information necessary to resolve the complex bathymetry does not exist on current NOAA navigation charts. The Bay's expansive shallow water characteristics renders much of it inaccessible by conventional waterborne survey methods. Obtaining this information requires an alternative survey technology capable of covering large shallow water areas and producing high resolution bathymetric data. During the spring of 1994 the SHOALS (Scanning Hydrographic Operational Airborne Lidar Survey) system was employed by NOAA to test its ability to resolve the complex shallow water bathymetry for a test area in central Florida Bay. Approximately 13 km of area was surveyed with a total surveying time of 12 hours. The data set presented here demonstrates that airborne lidar bathymetric technology such as SHOALS can be a valuable and cost effective tool for surveying large shallow water areas, without damage to the environment, that are otherwise inaccessible by conventional methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.992 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier 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".