Bibliographic record
Abstract
Abstract The microstructure of a material is related directly to its physical, chemical, and mechanical properties as they are influenced by processing and/or the environment. Among the numerous investigative techniques used to study materials, optical microscopy, with its several diverse variations, is important to the researcher and/or materials engineer for obtaining information concerning the structural state of a material. Information gained using optical microscopy is complementary to other techniques and provides unique information to assess the microstructure of the sample. Though predominantly qualitative, in some cases quantitative measurements are made: some examples are for quantification of grain growth, the coarsening of precipitates during a process, or the evolution of structural domains or defects. The goal of this article is to discuss the basic principles and structure of an optical microscope, including the light path, objective lenses, and illumination sources. In this new revised version of the article, fluorescence microscopy and its associated instrumentation are introduced. Practical tips for choosing a microscope, objectives, and accessories and for good microscopy practices are then given. A new Protocols section provides step‐by‐step guides to adjusting illumination and to some fluorescence applications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.043 |
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".