Tapeworm discovery in elasmobranch fishes: quantifying patterns and identifying their correlates
Bibliographic record
Abstract
Most parasites from known host species are yet to be discovered and described, let alone those from host species not yet known to science. Here, we use tapeworms of elasmobranchs to identify factors influencing their discovery and explaining the time lag between the descriptions of elasmobranch hosts and their respective tapeworm parasites. The dataset included 918 tapeworm species from 290 elasmobranch species. Data were analysed using linear mixed-effects models. Our findings indicated that we are currently in the midst of the greatest rate of discovery for tapeworms exploiting elasmobranchs. We identified tapeworm size, year of discovery of the type host, host latitudinal range and type locality of the parasite influencing most on the probability of discovery of tapeworms from elasmobranchs and the average time lag between descriptions of elasmobranchs and their tapeworms. The time lag between descriptions is decreasing progressively, but, at current rates and number of taxonomic experts, it will take two centuries to clear the backlog of undescribed tapeworms from known elasmobranch species. Given that the number of new elasmobranch species described each year is on the rise, we need to re-assess funding strategies to save elasmobranchs (and, thus, their tapeworm parasites) before they go extinct.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".