Parasite species richness in New Zealand fishes: a grossly underestimated component of biodiversity?
Bibliographic record
Abstract
ABSTRACT Estimates of total parasite species richness, for given host groups in given geographical areas, are always much higher than the numbers of known parasite species on which they are based. The discrepancy is a reflection of our limited current knowledge of parasite diversity. This is illustrated by a comparison of the parasite faunas of New Zealand fish species with those of Canadian fish; the latter have been well studied by fish parasitologists, and provide a standard for comparisons. More parasite species are known per host species for Canadian fish than for New Zealand fish, for both marine and freshwater fishes. This difference remains after correcting for differences in study effort, i.e. in the number of published studies per fish species. There are also more parasite species per fish species in Canada than in New Zealand when parasite species richness is expressed as number of species per unit of host body length. For freshwater fish, the difference can be explained by the restricted phylogenetic origins and geographical isolation of New Zealand fish species. For marine fish, however, there is no a priori reason to expect a difference in parasite species richness between fish in New Zealand and Canadian waters, and the observed difference probably results from a lack of appropriate parasitological surveys in New Zealand. If the true species richness of the parasite faunas of New Zealand marine fish species approaches that of their Canadian counterparts, then most of the diversity and ecosystem function of fish parasites in New Zealand remains unknown.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".