CAMPSITE USE LEVELS COMPARED TO CAMPSITE ATTRIBUTES IN EMILY PROVINCIAL PARK, ONTARIO
Bibliographic record
Abstract
This study used GIS and statistical analysis to examine the relationship between campsite and campground attributes and campsite use level. The hypothesis of the study was that campers choose campsites because of certain desirable attributes of the site and of its location within the campground. Emily Provincial Park in Ontario was the case study site. A database connected to a GIS contained data on 15 predetermined campsite attributes. The GIS also enabled the calculation of campground spatial attributes. The campsite use data, the number of nights the campsite was used in 1999, were used as the dependent variable to which all other variables were compared. The analysis found that campers utilise some campsite and campground amenities and attributes more than others when selecting their campsite. The statistical analysis of the campsite attributes revealed that campsite use level, as measured by the average number of camper nights per campsite, is significantly higher (p<.05) for each of the following characteristics: 1) availability of electricity, 2) higher levels of site privacy, 3) greater size of site, 4) the ability of site to allow vehicle pull through, 5) partial levels of shade, 6) ground slope less than 20%, and 7) overall quality of site. Camper use level is not significantly different with the following
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".