Histological processing of octocoral tissue v1
Bibliographic record
Abstract
In recent years, there has been a significant focus on coral habitats, encompassing both shallow and deep ecosystems. Historically, most studies concentrated on scleractinian corals, however contemporary research acknowledges the ecological importance of octocoral species, and the need to improve our understanding of their biology. Histology, which describes the internal morphology of animal tissues, is an essential tool for a wide range of biological studies, including reproductive biology, life history, taxonomy and phylogeny. Processing octocoral tissue histologically can be a complex task, given the unique skeletal structure of these organisms. Octocorals have internal skeletal structures called sclerites, which are composed of calcium carbonate and are embedded into the coral tissue. Decalcification of these structures creates air pockets in the tissue that hault further histological processing. Moreover, many octocorals possess a skeletal axis that is predominantly composed of organic matter, but may contain varying degrees of calcium carbonate. Including the axis in histological analysis may require experimentation to adjust the processing times. Although several studies report processing times for histological analysis, it is difficult to find detailed information on how to process octocoral tissue. Here, we present a detailed protocol that separates octocoral polyps from the skeletal axis, and utilizes vacuum to address issues with decalcification. The use of single polyps, or small polyp groups, decreases the processing time, and allows for manual processing without the use of automated systems. Vacuum removes trapped air from decalcification and increases the quality of the histological sections. We also include an optional step for specimens that were directly preserved in ethanol without prior fixation. Lastly, we demonstrate simple ways to section paraffin blocks that do not require extensive experience in histological sectioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".