The science and affect of atmosphere in landscape architecture
Bibliographic record
Abstract
Atmosphere carries multi-faceted meaning when considered in the context of spatial design. In an architectural sense, we may speak of atmosphere as a spatial quality or in the way the built or natural environment is capable of moving us emotionally. Yet, when considered in a scientific register, atmosphere may be described as a complex of observable and measurable energies, which give air substance, behavior and force. The practice of landscape architecture entails a heightened awareness of exposure, namely the exposure to meteorological processes that in turn shape much of our perceptual and haptic experience of the ‘outside’ world. The intent of this practicum will be to draw attention to the importance of both designations of atmosphere, particularly within the discipline of landscape architecture, and set within the context of phenomenology. The context of this work begins at the scale of the circumpolar boreal forest and examines a particular biological and chemical phenomenon that occurs between the atmosphere and the boreal forest biome. The scale of focus will be drawn to a site at the southern transition zone between the boreal forest and St. Lawrence mixed forest within the Temagami region of northeast Ontario, Canada. Here, the phenomenon in question is quite palpable.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".