Bibliographic record
Abstract
In 2013, the Province of Nova Scotia began an effort to modernize its coordinate referencing infrastructure known as the Nova Scotia Coordinate Referencing System (NSCRS). At that time, 8, active GPS stations were installed in southwest Nova Scotia to evaluate the technology's ability to address the Province's coordinate referencing needs. The success of the test phase helped build a business case to implement the technology across the entire Province. It is anticipated that by the end of 2015, 40 active GPS stations will be in place across Nova Scotia. This infrastructure, known as the Nova Scotia Active Control Stations (NSACS) network, will allow for instantaneous, centimetre level positioning across the Province. Originally designed to address the needs of the surveying community, the technology has also proven to have applications in mapping, machine automation, agriculture, navigation, emergency response, earthquake detection and other areas. In the foreseeable future, all spatial data sets captured in Nova Scotia will be either directly or indirectly derived from the NSACS network. The technology will promote high accuracy and homogenous spatial data sets across the Province. The technology behind the NSACS and the development of the system are described. Examples of how the technology is contributing to a digital Nova Scotia are presented. Future applications of the technology are also considered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".