Rendering falling snow using an inverse Fourier transform
Bibliographic record
Abstract
are not used in order to enforce an upper bound on size of moving image structure -- that is, snowflakes are small. Spatial frequencies above 128 cycles per frame are not used in order to stay far from the Nyquist limit. For spatial frequencies between 16 and 128 cycles per frame, we assign power proportional to 1 # # 2 . This puts a constant amount of power in each constant octave band [Field 1987] over the bands used. For each spatial frequency, we then randomize the phase, subject to the conjugacy constraint [Bracewell 1965] for a function I(x,y, t) and its 3D Fourier transform, I(# x , # y , # t ), namely: I(# x , # y , # t ) = I(-# x , y , t ) The second step is to take the inverse 3D Fourier transform of I(# x , # y , # t ), giving I(x,y, t). We rescale I(x,y, t) to have values in [0, 1], and treat the scaled result as an opacity function #(x,y, t). # e-mail: langer@cim.mcgill.ca e-mail: qiao@cim.mcgill.ca We use #(x,y, t) to composite a constant intensity s
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".