Bibliographic record
Abstract
vector in this case.It will also be used to denote quantum states later in the thesis.A detailed description of the so-called Dirac notation can be found in ref. [10, pp.108-163].|H and |V are orthogonal and form a basis.Any other polarization can be described as a linear combination of these basis vectors.where a and b are arbitrary complex numbers.Three different basis sets can be defined.H/V which was already introduced, D/A which stands for diagonal and anti-diagonal linearly polarized, i.e. the polarization vector is at 45 • between H and V and R/L, which stands for right-circularly and left-circularly polarized, respectively.The bases arewhere 1 and 2 label the first and second photon, respectively.This notation is convenient, because one can immediately see, that if photon 1 is measured to be |H polarized, also photon 2 will be |H polarized and if photon 1 is measured to be |V polarized, also photon 2 will be |V polarized.Up to this point nothing of the explanation necessarily requires quantum mechanics.I could prepare two bags with balls, where in the first bag all balls are red and in the second bag all balls are blue.If I now pick two balls from an unknown bag, I will find that either both are red or both are blue.But what can be done with the state above, but not with balls, is to write it down in a different basis.In the D/A basis the state will have the formAs above, if photon 1 is measured to be |D polarized, also photon 2 will be |D polarized and if photon 1 is measured to be |A polarized, also photon 2 will be |A polarized.This is a surprising result because the basis is just a reference frame that was chosen arbitrary and was changed after we defined which state the photon has.If we try to do this with the example of the colored balls, it would mean that two bags with red and blue balls are prepared.Afterwards, the question is changed to: "Are the balls green or pink?"And the result is that if the first ball was green the second will be as well.That does not make too much sense.The state |φ + is entangled whereas balls cannot be entangled because they are classical objects, which can always be described individually.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".