The use of visual information in natural scenes
Bibliographic record
Abstract
Despite the complexity and diversity of natural scenes, humans are very fast and accurate at identifying basic-level scene categories. In this paper we develop a new technique (based on Bubbles, Gosselin & Schyns, 2001a; Schyns, Bonnar, & Gosselin, 2002) to determine some of the information requirements of basic-level scene categorizations. Using 2400 scenes from an established scene database (Oliva & Torralba, 2001), the algorithm randomly samples the Fourier coefficients of the phase spectrum. Sampled Fourier coefficients retain their original phase while the phase of nonsampled coefficients is replaced with that of white noise. Observers categorized the stimuli into 8 basic-level categories. The location of the sampled Fourier coefficients leading to correct categorizations was recorded per trial. Statistical analyses revealed the major scales and orientations of the phase spectrum that observers used to distinguish scene categories.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".