Bibliographic record
Abstract
In the past, the planning of airports has largely been an intuitive process, leading to an often serious misallocation of resources. It is the contention of this thesis that the adoption of a more economic and integrated method of evaluating alternative airport plans could eliminate some of this mis-investment, and that the groundwork for such an evaluation process has already been done in the field of port planning. The evaluation method proposed is the use of a mathematical model of the airport's operation and of the benefit and cost interrelationships arising from the activities performed. The model can then be used to simulate the value of the benefits and costs of a number of possible alternative plans. It is the purpose of this thesis to discuss the applicability of the port model as a tool for airport planning and to point out the ease with which it could be applied both from the point of view of modifications and data requirements and availability. As background to the evaluation process, Chapter 2 presents some general theory and problems of economic evaluation and of the measurement of benefits and costs. Chapter 3 presents a description of planning processes currently being used by the Department of Transport in planning Canada's airports and points out some of the flaws in this approach. Chapter 4 then describes the type of port model now developed in so far as it can be used to determine interrelationships between investment, cost to ships of using the port, cost of port operation, and net community benefits. The calculations derived from the application of the model can then be used to determine the net present value of the benefit and cost streams arising from alternative ways of achieving a given level of port output, and thus to select the best possible combination of facilities. Chapter 5 then points out the similarities and differences between port and airport operation and hence the applicability of and the modifications required in the application of the port model to airport planning situations. The remainder of the chapter delineates the type of data required to construct and use an airport model and the availability of this data to the airport planners. Finally, Chapter 6 summarizes the findings and concludes that, while it has its limitations as a terminal model, as a representation of airport operation and as an evaluation process, the port model can be adapted relatively easily to airport planning to provide a more integrated, more economic approach to the evaluation of alternative airport plans.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".