Il paesaggio e le citta della costa est del Canada. Un viaggio tra luci, colori e materiali.
Bibliographic record
Abstract
In the Americas, natural and anthropogenic landscapes are sometimes together a small and authentic universe of color whose\nproperties are manifested in terms of physical, perceptual and physiological; the natural and human tissue is identified visually and\nformally through its chromaticity. Spaces contributes to the identification of light and color, the perception of which varies\naccording to natural factors (exposure, latitude and altitude - day/night, seasons, weather conditions) and artificial (materials,\ntextures, etc. ..). In these conditions you can find the "color areas", even unconsciously perceived, characterized by the effects they\nproduce (comfort, oppression, closed, barrier) or simply by their aesthetic value. A similar process is recorded in our sensitivity\nwhen - in an urban environment – basing on a "favorable" perception, we favor a "color area" among others. The contribution wants\nto propose, through a series of sketches and drawings, a sequence of color images made by the author during a trip of over 1000 km,\ndivided between the cities of Quebec, Toronto, Montreal, Ottawa, etc.. , and the boundless landscapes of the Canadian parks, on that\noccasion there was a wide variety of chromatic effects, linked both to nature (such as atmospheric, the color of the sky, sunrise\nsunset, etc..) and to the intervention of man.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.010 |
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".