Conducting a Water Recreation Opportunity Spectrum Inventory on the Northern Forest Canoe Trail in New Hampshire
Bibliographic record
Abstract
In 1999, the National Recreation Lakes Study Commission reported that the nation’s nearly 1,800 lakes hosted some 900 million visits (each with one or more visitors) a year and generated more than $44 billion in economic impacts (National Recreation Lakes Study Commission, 1999). This is an all-time high for visitation and has been coupled with increased pressures for shoreline development, and managers are faced with the difficult task of making robust and defensible management decisions that can be understood and supported by the public (Haas & Aukerman, 2006). New Hampshire is among those states that have seen dramatic increases in both popularity and use of water recreation areas and is in need of more information to make sound management decisions for its water resources (New Hampshire SCORP, 2007). One unique water resource within the state is the Northern Forest Canoe Trail (NFCT). This is an historic 740-mile stretch of connected waterways that pass through New York, Vermont, Quebec, New Hampshire, and Maine, and offer many recreation opportunities for boating, angling, sight seeing, swimming, and camping. Along with this diversity of use and increased popularity, the managers of the NFCT now seek information that will help to them understand and classify the recreation settings and subsequent opportunities. In order to improve management of water resources, the Bureau of Reclamation, with support of the Federal Lakes Recreation Leadership Council and hundreds of lake managers across the Nation, has recently completed the development of a Water Recreation Opportunity Spectrum (WROS) system (Haas & Aukerman, 2006). This paper describes how a WROS inventory was completed on the 75- mile long NH section of the NFCT and how water recreation managers can use a WROS inventory to help them to: (a) better understand their own resource and facilitate communication with visitors by improving maps and information regarding the type of recreation experiences to be offered at different locations; (b) develop future management direction and justify actions based on regional planning and understandable information; (c) provide a visual understanding of how proposed development or adjacent changes may affect the recreation opportunities available; and (d) explain rules and regulations more clearly as 109 to why certain actions are desirable to protect the integrity of specific recreation opportunities. Additional uses and benefits of WROS to water recreation management are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".