<p><strong>A small gear to enhance the observation of amber inclusions</strong></p>
Bibliographic record
Abstract
Paleoentomologists well know that the examination of fossil insects in amber is often complicated. Even if the amber is well-polished, the presence of small scratches at the surface and inner impurities generates reflections that limit observations. Prominent characters for taxonomy thus may not be visible even when preserved. Several solutions have been proposed to enhance the observation of fossil insects in amber. Most of them aim at limiting the number of optical medium interfaces, and thus reducing optical artefacts such as refraction and reflection. For example, amber may be embedded in Canada balsam or artificial resins (Azar & Nel, 1998; Green, 2001; Sidorchuk, 2013; Penney & Jepson, 2014; Sidorchuk & Vorontsov, 2018). However, these methods limit viewing angles and thus the observation of characters, are technically challenging and often irreversible. Another type of method is to immerse the piece of amber in a liquid with a refractive index as close as possible to the amber, such as sugared water, or oils (Sidorchuk, 2013). In this case, the interface between the objective lens and the amber is the coverslip that is placed on the surface of the liquid in order to avoid reflection on the water surface. Compounding the difficulty in observation is that an immersed piece of amber is observable only from one angle at a time, and the whole setting must be dismantled and reassembled to manually change the observation angle, which can result in a lengthy procedure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.094 | 0.045 |
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".