Bibliographic record
Abstract
Despite its long practical history landscape architecture is still young as an academic discipline. In Poland the interest in landscape architecture began to grow in the last quarter of the 20th century and only recently became really vivid. The studies cover a wide range of subjects connected mainly with natural science. Students also get the chance to develop their artistic skills through the medium of art classes. Students of landscape architecture obtain a Master of Science degree, therefore technical subjects such as geometry or engineering graphics remain particularly significant. Teaching methods which make the taught subject relevant to the future occupations of the students, or are closely related to the faculty, draw a favourable response from the students. The article presents the whole range of examples of tasks, which are relevant to the profession connected with the landscape architecture and outlines the issues which are taught in descriptive and engineering geometry classes. Nowadays, it is difficult to observe students' craving for purely theoretical academic knowledge. To be well received it has to be ‘smuggled’ under the guise of practical use and immediate usefulness in their vocation. Anyway, it seems that what is most important is the result. Theoretical knowledge should be a good foundation for the future work of graduates and will hopefully develop them intellectually.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".