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<p><strong>A small gear to enhance the observation of amber inclusions</strong></p>

2020· article· en· W3023001628 on OpenAlexaboutno aff
Thomas Schubnel, Valérie Ngô‐Muller, André Nel

Bibliographic record

VenuePalaeoentomology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFossil Insects in Amber
Canadian institutionsnot available
Fundersnot available
KeywordsOpticsLimitingReflection (computer programming)RefractionTotal internal reflectionRefractive indexChemistryMaterials sciencePhysicsComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.038
GPT teacher head0.261
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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