Bibliographic record
Abstract
We present an algorithm for determining the dielectric autocorrelation function of a disordered medium from angle resolved multiple light scattering measurements. Photons propagating in a disordered, multiple scattering medium are classified as being either ballistic, “snake-like” or diffusive, depending on the nature of their trajectory between source and detector. Considerable information about the nature of the scattering medium is contained in the early arriving snake-like photons whereas this information is smeared in the late-arriving diffusive photons. We derive from first principle a formal mathematical description which encompasses all three regimes of transport and relates the experimentally observed light intensity correlation functions to the ensemble averaged autocorrelation function <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="eq1" display="inline"> <mml:mrow> <mml:mover accent="true"> <mml:mi>B</mml:mi> <mml:mo>˜</mml:mo> </mml:mover> <mml:mo stretchy="false">(</mml:mo> <mml:mover accent="true"> <mml:mi>x</mml:mi> <mml:mo>→</mml:mo> </mml:mover> <mml:mo>,</mml:mo> <mml:mover accent="true"> <mml:mi>y</mml:mi> <mml:mo>→</mml:mo> </mml:mover> <mml:mo stretchy="false">)</mml:mo> <mml:mo>≡</mml:mo> <mml:msub> <mml:mrow> <mml:mrow> <mml:mo>〈</mml:mo> <mml:mrow> <mml:msup> <mml:mi>ε</mml:mi> <mml:mo>*</mml:mo> </mml:msup> <mml:mo stretchy="false">(</mml:mo> <mml:mover accent="true"> <mml:mi>x</mml:mi> <mml:mo>→</mml:mo> </mml:mover> <mml:mo stretchy="false">)</mml:mo> <mml:mi>ε</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mover accent="true"> <mml:mi>y</mml:mi> <mml:mo>→</mml:mo> </mml:mover> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>〉</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mtext>ens</mml:mtext> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> for a disordered medium with dielectric constant <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="eq2" display="inline"> <mml:mrow> <mml:mi>ε</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mover accent="true"> <mml:mi>x</mml:mi> <mml:mo>→</mml:mo> </mml:mover> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> .
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".