A guide to methods for estimating phago-mixotrophy in nanophytoplankton
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Growing attention to phytoplankton mixotrophy as a trophic strategy has led to significant revisions of traditional pelagic food web models and ecosystem functioning. Although some empirical estimates of mixotrophy do exist, a much broader set of in situ measurements are required to (i) identify which organisms are acting as mixotrophs in real time and to (ii) assess the contribution of their heterotrophy to biogeochemical cycling. Estimates are needed through time and across space to evaluate which environmental conditions or habitats favour mixotrophy: conditions still largely unknown. We review methodologies currently available to plankton ecologists to undertake estimates of plankton mixotrophy, in particular nanophytoplankton phago-mixotrophy. Methods are based largely on fluorescent or isotopic tracers, but also take advantage of genomics to identify phylotypes and function. We also suggest novel methods on the cusp of use for phago-mixotrophy assessment, including single-cell measurements improving our capacity to estimate mixotrophic activity and rates in wild plankton communities down to the single-cell level. Future methods will benefit from advances in nanotechnology, micromanipulation and microscopy combined with stable isotope and genomic methodologies. Improved estimates of mixotrophy will enable more reliable models to predict changes in food web structure and biogeochemical flows in a rapidly changing world.
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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 | 0.006 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it