Bibliographic record
Abstract
The context of this manuscript is metrology, defined as the study of measurement.Within this scope, metrology focuses on the setting of standards.In 1795, French revolutionaries proposed standards for length, volume, weight, and money.In 1875, France established a permanent body to oversee the Bureau Internationale des Poids et Mesures (BIPM), which defines units of measurement.More specifically, the context of this manuscript is nanometrology, the study of measurement at nano (less than 100 nm) scales.Nanometrology is a 21 st century extension of the science of measurement (Hand 2016), necessitated by the emerging capacity to measure at these scales, together with increasing applications, notably quantum computing, nanoscale engineering, and nanoscale medicine.At present, the development of protocols and standards in nanomaterials is in its early stages.It is not yet clear how these standards should be developed (Jorio and Dresselhaus 2007).A review of current techniques treats atomic structure (electron diffraction, small angle x-ray scattering, x-ray absorption spectroscopy), with listings of limitations (Herrera-Basurto and Simonet 2013).The transition from single lab to general application is still in the early stages.For example, a one-size-fits-all metrology solution for ultra-thin 2-D structures does not yet exist (Celano et al. 2024).The Measurement Mechanics manuscript addresses a problem not mentioned in current reviews of nanometrology.Measurement at nano scales necessarily disturbs the object --it can change its state (Heisenberg 1927).Nanometrology thus faces a quandary encountered in other sciences (Hand 2004 Ch4.5).In biology, for example, multiple mark-recapture studies alter the catchability of an animal and hence alter estimates of population size and mortality rates.In demography, the presence of a question about citizenship alters the probability of completion of a form.The question potentially influences answers on the form.In ethology, the presence of an observer alters the behavior of objects of study such as birds and mammals.While these examples are not quantum entanglement, the observer effect was used by Heisenberg as a physical "explanation" of quantum uncertainty (Heisenberg 1930, p20).These examples are relevant because the MM manuscript states its applicability across the sciences.How relevant, then, are Heisenberg's examples beyond quantum mechanics?One limitation is that in principle, quantum effects can be Qeios, CC-BY 4.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".