Natural history collections: collaborative opportunities and important sources of information about helminth biodiversity in New Zealand
Bibliographic record
Abstract
ABSTRACT Only a small fraction of the Earth’s total biodiversity has been described. This is particularly true of parasitic fauna, due to the paucity of taxonomic expertise, funding, and interest in parasites. It is expected that co‐extinctions will become the main cause of species loss with potentially half of the parasite species becoming extinct prior to their discovery. This article addresses this issue and highlights case studies from the Otago Museum (OMNZ) (Dunedin, New Zealand), providing examples of successful collaborations between government organisations, museums, and parasitologists in bridging knowledge gaps in parasite diversity. The case studies presented focus on the parasitic helminths from opportunistic necropsies of stranded marine mammals and deceased birds. Collections from these case studies have doubled the size of the parasite collection at the OM, making this institution the most important repository of parasitic helminths in the country. We encourage such collaborations between museums, governing bodies, indigenous communities, ecologists and parasitologists in enhancing our knowledge of parasite diversity. Furthermore, we urge scientists to deposit both host and parasite tissues from surveys, vouchers, along with their respective metadata so that samples can be adequately stored and curated, thus ensuring that parasite collections become a legacy for future generations of scientists.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".