Minimalist barcodes for sponges: a case study classifying African freshwater Spongillida
Bibliographic record
Abstract
African sponges, particularly freshwater sponges, are understudied relative to demosponges in most other geographical regions. Freshwater sponges (Spongillida) likely share a common ancestor; however, their evolutionary history, particularly during their radiation into endemic and allegedly cosmopolitan groups, is unclear. Freshwater sponges of at least 58 species of 17 genera and four families are described from Central and Eastern Africa, but the diversity is underestimated due to limited distinguishable morphological features. The discovery of additional cryptic species is very likely with the use of molecular techniques such as DNA barcoding. The Royal Museum of Central Africa (MRAC, Tervuren, Belgium) hosts one of the largest collections of (Central) African freshwater sponge type material. Type specimens in theory constitute ideal targets for molecular taxonomy; however, the success is frequently hampered by DNA degradation and deamination, which are a consequence of suboptimal preservation techniques. Therefore, we genotyped African demosponge holotype material of the MRAC with specific short primers suitable for degenerated tissue and compare the results with the current, morphology-based classification. Our results demonstrate the utility of minimalistic barcodes for identification of sponges, potentially enabling efficient identification of individuals in taxonomic or metabarcoding studies, and highlight inconsistencies in the current freshwater sponge classification.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".