Bibliographic record
Abstract
The protection and enhancement of visual resources constitute an on-going challenge to the planning authorities in many communities. The crux of this challenge is to guide development towards built and natural landscape forms that will not cause detriment to an existing landscape character. To understand and cope with this problem, there is the need for a means to define and model a landscape's character, to identify methods for constructing that character definition, to create tools for storing and using such a definition to visualize its spatial manifestations, and to incorporate alternative development regulatory parameters in order to assess their impact on landscape character. \nCurrent spatial data technologies are able to portray inventories of specific, real-world objects. While well established in the planning profession, these technologies and their attendant data manipulation tools do not easily facilitate the creation of generalized, non-specific statements that are applicable across a region. Such generalized statements regarding visual and spatial features are at the heart of descriptions of landscape character and implicit within most planning regulations intended to produce a desirable landscape character. Current spatial data tools therefore do not satisfy the stated needs of planning for landscape character. \nIn satisfying these conceptual, methodological and technological deficiencies, the research presented in this dissertation defines and demonstrates a theory of landscape grammar which formally draws parallels between the structures of linguistics and the character of landscapes. A landscape grammar defines a landscape character using a spatial vocabulary and syntax rules and can be applied to a site to generate landscape forms that embody the defined character. In this dissertation, the spatial counterparts of the linguistic concepts of vocabulary and grammar rules are formalized and implemented for use in a custom-developed geographic information system. Methods that enable the use of landscape grammars in a planning environment are presented and subsequently applied through the formal expression of planning regulations into the grammar-based model. The theory, methods and software implementation are demonstrated using a residential area of the island of Bermuda. The iterative grammatical generation of an example two-dimensional landscape scene is demonstrated with further three-dimensional representations of the results for visualization purposes. Alternative planning regulations are also incorporated into the case study grammar and resultant three-dimensional landscapes are shown. Several suggestions for future research on landscape grammars are offered in the conclusions of the dissertation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.014 |
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".