Qualitative Characterization of Archachatina marginata Varieties in the Derived Savannah Zone of Ogun State, Nigeria
Bibliographic record
Abstract
The Giant African Land Snail is one of the largest land snails in the world. In Nigeria, farmers still can not identify the breed Archachatina marginata (Am) and its varieties. In this study, Archachatina marginata varieties were characterized using qualitative characters. A total of 304 animals comprising of 93 Archachatina marginata marginata (Amm), 163 Archachatina marginata suturalis (Ams) and 48 Archachatina marginata ovum (Amo) was used. Five qualitative parameters were collected on each snail and recorded. These parameters are shape of snail (SS), shell colour (SC), colour of labium (CL), foot colour (FC), number of whorls (NW). Data were analysed using simple descriptive statistics; frequency counts and percentages. The orange labium was more than white labium. 87.0%, 86.50%, 77.08% was orange labium in Amm, Ams and Amo respectively. 70.96% of Amm had brown foot, 54.16% of Amo had brown foot, 50.92% are black foot and 49.08% brown foot in the Ams All the varieties were sinistral (100%) in shape. 100% of the shell colour of Amm was yellow shell with black straight streaks. 52.69%, 68.08%, 64.58% had 3+2 whorls in Amm, Ams and Amo respectively. This study has revealed that these snails can be identified by these qualitative characteristics apart from shell colour. The labium and foot colour can be used to identify these varieties. The labium and foot colour has also indicated that there are still variations among the varieties. Therefore, there is a need for further studies on these varieties of Archachatina marginata.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".