Bibliographic record
Abstract
Land subdivision is an important activity in surveying engineering. Given a polygon-shaped area of land, the objective is normally to divide the land into two areas with specific requirements. Existing methods for land subdivision are applicable only when the sides of the land are all straight lines. This paper presents a unified direct method that can handle tracts of land with one or more circular sides. The ends of the partitioning line are assumed to connect straight and circular sides. Using a Cartesian coordinate system, three formulas representing the foundation of the proposed method were developed: (1) the function of the circular side; (2) the intersection between the partitioning line and the circular side; and (3) the area of the circular segment which is cut by the partitioning line. The resulting nonlinear problem was solved using the Excel-based, Premium Solver software. Any partitioning case can be handled by a simple change to the worksheet. Tracts of land with all-straight sides are special cases that correspond to a very large radius of the circular side. In addition, the proposed method can handle optimization objectives that cannot be addressed by existing methods, such as minimizing the difference between the frontages of the partitioned parts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".