Washington Spatial Reference Network expands
Bibliographic record
Abstract
The Washington Spatial Reference Network (WSRN) has expanded considerably during the last six months (see attached map). There are now 50 of the planned 80 stations providing data to the network, of which 43 are included in the network solution. Currently these stations are divided into four separate sub-networks designed to provide VRS solutions. These sub-networks can be defined as the Puget Sound, NE-Spokane area, SE- TriCities area, and SW-Vancouver/Portland area. The network has expanded beyond Washington boarders to include stations in British Columbia, Oregon, and Idaho insuring complete coverage up to our state perimeters. Special thanks must go to Gavin Schrock of Seattle Public Utilities for the many road trips pursuing infrastructure sites and contacts while working out detailed communication issues. Washington is the only state in the nation to construct a Real-time GPS network as a cooperative. While we are possibly the marvel of the world to do so, the additional effort involved in partnerships is extremely strenuous. Never the less, the desire to acquire the efficiency RTN GPS offers, has produced the type of public and private involvement needed to get us to this stage. With the increased utilization of the WSRN system comes the need for training and education. The subject of transforming GPS-derived coordinates to a local coordinate system such as the Washington State Plane Coordinate System or a Project Datum derivative, has become a highlighted need at WSDOT. The process known as "calibration or transformation " is essential to accuracy and compatibility of the RTN system output. The WSRN coordinate output are defined as NAD83CORS (Constant Operating Reference Stations), which can differ from NAD83/91 by amounts up to 0.3 tenths of a foot (see attachments).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.291 | 0.231 |
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".