Bibliographic record
Abstract
Investigators are interested in how landscape is presented and perceived.Often environments are presented and characterized selectively,ignoring some realities and promoting discerning impressions.This investigation focused upon cultural perceptions about landscape naturalness based upon primarily oil paintings,photographs,and post cards,as the artists and photographers make decisions about what to present and about what not to show,and what people expect to see in the landscape through the artist's work.In this study three landscapes are examined:the Loire River Valley in France,the Alentejo in Portugal,and the Laurentides in Quebec,Canada.They are studied by applying the artialisation analysis process to images of the study area to assess the content and structure and determine if a general and repetitive content and compositional structure are present.If such a content and composition repetition arises,the artialisation identifies the discerned impression or artistic for the area.This study found that each of the study areas contain a specific artialisation representation related to three myths:the myth of the last wild river in Europe,the Loire, the myth of the last wild garden in Europe,the Alentejo, and the original forest,the Laurentides.
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 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".