An introduction to the special issue: Oxyrrhis marina, a model organism?
Bibliographic record
Abstract
Many “model” protists are maintained in culture and used, experimentally, to answer questions associated with planktonic processes. Given the current interest and rapidly increasing amount of literature on the heterotrophic dinoflagellate Oxyrrhis marina, we present in this special issue a series of focused, interlinked research articles. Being written by experts in their respective fields, the authors have included unpublished data and in all cases have offered a synthesis of data and ideas. Furthermore, we have encouraged cross-paper discourse, emphasizing the interdisciplinary nature of our work and the utility of O. marina to this end; we also offer guidance, both practical and intellectual, on how future research related to O. marina might progress. In this introduction, however, we raise the wider issue of which criteria are required to consider a taxon as a “model species”. We then assess the extent to which O. marina can fill this role. In general, we recognize O. marina as a model in three distinct disciplines: ecology, evolution/genomics and biogeography. Of possibly greater importance, we recognize that if O. marina continues to be studied at an escalating rate, there will be a concomitant increase in realized and potential synergies across these fields.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".