ECOSYSTEM ENGINEERING BY BIOTURBATING POLYCHAETES IN EVENT BED MICROCOSMS
Bibliographic record
Abstract
Abstract Modification of event beds by the burrowing nereidid polychaete Alitta virens (Sars) was examined using laboratory microcosms, to assess its importance as an ecosystem engineer in pristine sediments. In all microcosms, the nereidids modified their environment to permit long-term occupation, but different behavioral strategies and burrow morphologies were observed based on sediment characteristics and nutrient availability. Alitta virens utilized scavenging, surface deposit feeding, suspension feeding, microbial gardening, deposit feeding at depth, and cannibalism. Suspension feeding using mucus nets is used by many nereidids but has not been documented previously in A. virens; extended use of the technique may indicate low availability of biotic sediments for deposit feeding. Alitta virens typically produced burrows similar to Arenicolites and Skolithos, but morphologies resembling Polykladichnus, Planolites, Palaeophycus, and Thalassinoides were formed under differing sedimentary conditions and over different time scales. In the rock record, such ichnological diversity might be interpreted as indicating paleoecological diversity, rather than the response of one taxon to changing conditions. Alitta virens is an allogenic ecosystem engineer, its behavior changing the physical and geochemical characters of its environment. These changes, combined with the widespread occurrence and population longevity of A. virens, demonstrate that burrowing polychaetes are important ecosystem engineers in shallow marine environments, and are likely to have been so over geological time scales.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".