User’s Guide to Vertical Control and Geodetic Leveling for CO-OPS Observing Systems.
Bibliographic record
Abstract
The National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS) is responsible for the management of a national water level measurement program. The foundation of this program is the operation and maintenance of the National Water Level Observation Network (NWLON), a network of approximately 200 continuously operating data collection stations in the U.S. coastal oceans, the Great Lakes and connecting waterways, and in U.S. Trust Territories and Possessions. The data and information from this network represent one of the most unique and valuable geophysical data sets available. The network provides for the determination and maintenance of vertical reference datums used for surveying and mapping, dredging, coastal construction and restoration, water level regulation, marine boundary determinations, tide prediction, and determination of long-term water level variations (e.g. trends). The station platforms and telemetered data are used to support major U.S. Government programs such as the NWS Tsunami Warning System, the NWS storm surge monitoring programs, the U.S. Army Corps of Engineers (USACE) national dredging program, the USACE/Canadian Great Lakes regulation program, and the NOAA Climate and Global Change Program. This guide provides references to several National Geodetic Survey (NGS) documents related to the standard methodologies and tools used to derive geodetic elevations using differential and trigonometric leveling. These references do not supersede the information in this document as this document is specifically written to determine and monitor water level sensor and bench mark network elevations for the determination of tidal datums and subsequently the sea level trend.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.276 | 0.282 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".