Current and Future Patterns of Land-Use Change in the Coastal Zone of New Jersey
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".