The energetic cost of filtration by demosponges and their behavioural response to ambient currents
Bibliographic record
Abstract
Sponges (Porifera) are abundant in most marine and freshwater ecosystems, and as suspension feeders they play a crucial role in filtering the water column. Their active pumping enables them to filter up to 900 times their body volume of water per hour, recycling nutrients and coupling a pelagic food supply with benthic communities. Despite the ecological importance of sponge filter feeding, little is known about how sponges control the water flow through their canal system or how much energy it costs to filter the water. Sponges have long been considered textbook examples of animals that use current-induced flow. We provide evidence that suggests that some species of demosponge do not use current-induced flow; rather, they respond behaviourally to increased ambient currents by reducing the volume of water filtered. Using a morphometric model of the canal system, we also show that filter feeding may be more energetically costly than previously thought. Measurements of volumetric flow rates and oxygen removal in five species of demosponge show that pumping rates are variable within and between species, with the more oxygen consumed the greater the volume filtered. Together, these data suggest that sponges have active control over the volume of water they process, which may be an adaptation to reduce the energetic cost of filtration in times of high stress.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".