Confocal laser scanning microscopy (CLSM) as a new tool for morphological characterisation of both newly collected and museum voucher specimens of the Trypanorhyncha Diesing, 1863 (Platyhelminthes: Cestoda)
Bibliographic record
Abstract
Taxonomic issues within Trypanorhyncha, e.g., the inaccurate light microscopic visualisation of the hook patterns, are solvable by confocal laser scanning microscopy (CLSM). We applied CLSM imaging to study Trygonicola macropora (Shipley et Hornell, 1906) and Dollfusiella michiae (Southwell, 1929) from Neotrygon caeruliopunctata Last, White et Séret from Bali, Indonesia. To illustrate the strength and limitations of CLSM, images of Otobothrium cysticum (Mayer, 1842) and Symbothriorhynchus tigaminacantha Palm, 2004, both permanent mounts from a collection, were also processed. The CLSM created image stacks of many layers, and edited with IMARIS Software, these layers resulted in three-dimensional images of the armature patterns and internal organs of both species. BABB (benzylalcohol and benzylbenzuolate) clearing was applied to T. macropora. We conclude that trypanorhynch cestodes stained with Mayer-Schuberg's acetic carmine permanently mounted in Canada balsam are suitable for CLSM, allowing detailed analyses of museum type-material as well as freshly collected and processed worms. BABB resulted in imaging the testes in detail, suggesting other stains to be used for CLSM in trypanorhynch cestode research. Application of CLSM for studies of other cestode groups is highly recommended.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".