Current and Future Patterns of Land-Use Change in the Coastal Zone of New Jersey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent urban development along the US coasts has negatively impacted the local environment, and these impacts will only increase thanks to rapid regional population growth. Empirical spatially disaggregate land-use models provide a way to explore future conditions and environmental impacts before irreversible changes occur. An assumption of many models is that access to urban-employment centers is the major factor locating urban uses within a region, the opposite of the pattern seen in most natural amenity rich areas. As a result, it is unclear whether models focusing on center accessibility can be used to predict future land-use patterns in urbanizing coastal regions. In this paper the relationship between accessibility and the location of urban development was examined for coastal New Jersey, USA. Two questions were addressed through the analysis: (1) Is accessibility to urban or employment centers correlated with the location of urban conversions? (2) If accessibility is correlated with the location of urban conversion, does the inclusion of such variables into a land-use-change model improve the ability of the model to locate future urban development? Results from the analysis indicate that traditional accessibility relationships can be used to explain the location of urban conversions in New Jersey's coastal region, but inclusion of accessibility and other locating factors does not necessarily improve the predictive ability of a model. The accessibility relationship is contrary to findings in many other high-amenity areas, because, in part, of the importance of access to the region's transportation network.
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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 | 0.000 | 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 it