From mice to elephants: overturning the ‘one size fits all’ paradigm in marine plankton food chains
Bibliographic record
Abstract
It is widely believed that consumer control is a weak regulator of marine phytoplankton communities. It remains unclear, however, why this should be the case when marine consumers routinely regulate their prey at higher trophic levels. One possibility is that the weak consumer control of phytoplankton communities results from the inability of field researchers to effectively account for consumer-prey trophic relationships operating at the scale of the plankton. We explored this issue by reviewing studies of trophic control in marine plankton. Experimental studies indicate that size is a critical determinant of feeding relationships among plankton. In sharp contrast, of the 51 field studies reviewed, 78% did not distinguish among the sizes or species of phytoplankton and their consumers, but instead assumed a general bulk phytoplankton-zooplankton trophic connection. Such an approach neglects the possibility that several trophic connections may separate the smallest phytoplankton (0.2 μm) from the larger zooplankton (~ 1000 μm), a remarkable size differential exceeding that between a mouse (~10 cm) and an elephant (~2500 cm). The size-based approach we propose integrates theory, experiments and field observations and has the potential to greatly enhance our understanding of the causes and consequences of recently documented restructuring of plankton communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".