Bibliographic record
Abstract
Sponges interact with most other organisms in marine systems as competitors, symbionts, hosts of symbionts, consumers, and prey. Considerable creative energy has been required to study and describe the amazing variety of sponge interactions, as sponges can hide symbionts deep inside, rapidly regenerate wounds from grazers, carry on important associations with unculturable microscopic organisms, and otherwise foil attempts to determine how they are interacting with other organisms. This review of sponge interactions covers (i) competition among sponge species, and between sponges and other sessile organisms; (ii) predation on sponges by sponge specialists and by opportunistic sponge feeders, and aspects of predation such as the importance of nutritional quality, trade-offs between growth and defense against predators, biogeographic patterns in predation, and the advantages of various techniques for studying predation; and (iii) symbiotic associations of sponges with a variety of organisms representing all types of life, and with results ranging from parasitism and disease to mutual benefit. A hint that some generalizations about ecological interactions of sponges may be possible is just becoming evident, as accumulating data appear to show taxonomic and geographic patterns; however, it is also clear that surprises will continue to emerge from every probing new study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".