The potential roles of sponges in integrated mariculture
Bibliographic record
Abstract
Abstract This mini‐review evaluates the use of marine sponges in integrated culture systems, two decades after the idea was first proposed. It was predicted that the concept would provide a double benefit: sponges would grow faster under higher organic loadings, and filtration by sponges would improve water quality. It is promising that the growth of some commercially interesting sponges is indeed faster in organically enriched areas. The applicability of sponges as filters for undesired microorganisms has been confirmed in laboratory studies. However, upscaled farming studies need to be done to demonstrate the value of sponges for in situ bioremediation of sewage discharge or waste produced by fish cages. In addition, a new idea is presented – the use of sponges as an engine to convert dissolved organic matter (DOM) into particulate organic matter (POM) that can be consumed by deposit feeders through a chain of processes termed the sponge loop. A theoretical design of an integrated culture with seaweeds ( Gracilaria sp.), sponges ( Halisarca caerulea ) and sea cucumbers ( Apostichopus japonica ) shows that 37% of the part of the primary production that is excreted by the seaweeds as DOM can be directly recovered in sponge biomass and a subsequent 12% in sea cucumber biomass after mediation (conversion of DOM to POM) by sponges. Hence, the total recovery of DOM into (sponge and sea cucumber) biomass within this IMTA is 49%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".