Scenic River Evaluation Projects: Clinch and Dan Rivers
Bibliographic record
Abstract
The Virginia Department of Conservation and Recreation (DCR) provides field work and evaluative documentation for qualifying Virginia jurisdictions who seek to designate local rivers in the state’s Scenic River Designation program. The Community Design Assistance Center (CDAC) was asked to assist DCR in the evaluation of two rivers. The two river segments under consideration for Scenic River Designation: • Clinch River (Tazewell County) – a 30.3 mile segment • Dan River (Halifax County) – a 38.6 mile segment Each river, or river segment, is evaluated and rated on fourteen different factors from which a cumulative score is derived. This score must meet a minimum rating score to be designated. Each factor was selected because of its ability to enhance the scenic experience and its contribution to the overall quality of the resource. CDAC staff worked with DCR personnel to collect background data, conduct field studies along the rivers, utilize the gathered data to determine eligibility for designation under the Virginia Scenic River Program, and prepare a report of the field data findings.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".