A dataset comprising four micro-computed tomography scans of freshly fixed and museum earthworm specimens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: ALTHOUGH MOLECULAR TOOLS ARE INCREASINGLY EMPLOYED TO DECIPHER INVERTEBRATE SYSTEMATICS, EARTHWORM (ANNELIDA: Clitellata: 'Oligochaeta') taxonomy is still largely based on conventional dissection, resulting in data that are mostly unsuitable for dissemination through online databases. In order to evaluate if micro-computed tomography (μCT) in combination with soft tissue staining techniques could be used to expand the existing set of tools available for studying internal and external structures of earthworms, μCT scans of freshly fixed and museum specimens were gathered. FINDINGS: Scout images revealed full penetration of tissues by the staining agent. The attained isotropic voxel resolutions permit identification of internal and external structures conventionally used in earthworm taxonomy. The μCT projection and reconstruction images have been deposited in the online data repository GigaDB and are publicly available for download. CONCLUSIONS: The dataset presented here shows that earthworms constitute suitable candidates for μCT scanning in combination with soft tissue staining. Not only are the data comparable to results derived from traditional dissection techniques, but due to their digital nature the data also permit computer-based interactive exploration of earthworm morphology and anatomy. The approach pursued here can be applied to freshly fixed as well as museum specimens, which is of particular importance when considering the use of rare or valuable material. Finally, a number of aspects related to the deposition of digital morphological data are briefly discussed.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it