Bibliographic record
Abstract
It is generally admitted that the relative location of an aerosol between an observation device and the observed scene will have an influence on the detected image quality. These effects are usually classified under the label “shower curtain effect” (SCE). The usual formulation describing it is as follows: an observer standing away from a shower curtain can detect the presence of a person standing just behind it whereas the opposite is not true. Starting from a discussion of experimental results which seemed to invalidate the SCE, we show that it is not the only mechanism at work and that thorough analysis of the measurement setup is required before reaching such conclusion. We base our discussion on four cases, two of them of the passive detection type, the two others being of the active type. We also show that the ratio of scattered to unscattered light at the detector is of utmost importance. We show this by further developing our model [10] of the point spread function (PSF) of the receiver. This model allows the discussion of the SCE in the frequency domain in terms of the cuton and cutoff frequencies of the receiver. In the end, we show that the apparent paradoxical results we had found cannot actually be placed under the “shower curtain effect” denomination because: 1-) the amount of unscattered light captured is higher than the amount of scattered light, and 2-) the receiver cuton frequency is much higher than the aerosol cutoff frequency rendering most mechanisms of the shower curtain effect ineffective.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".