Bibliographic record
Abstract
AM screening (halftoning) often suffers from periodic patterns in single separation. Since these disturbing patterns become stronger as the printer's resolution decreases, they pose a real challenge to laser printers & inkjets as they compete against offset presses.We present a formula for predicting the frequencies and amplitudes of these disturbing patterns, based only on the geometric structure of the screen. An automatic filter, based on this formula, was constructed. This filter passes only 4% of the potential screens, without the need to construct the screen matrices, and without print, thus reduces testing time drastically. At the next stage, the method was generalized to handle interference between the screen and machine frequencies.This filter became a vital tool in screening development for HP-Indigo machines. It served us well in the construction of all of our latest high ruling screens. Currently, this tool is also used to generate an AM screen for the commercial inkjet developed by HP-Vancouver, and the results are promising.A patent application was submitted, concerning both the filter and the fine geometries which it passes (international application PCT/IL00/00079, publication number WO0158140, filed on 06/02/2000).
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".