Simulated fiddler-crab sediment mixing
Bibliographic record
Abstract
Using a lattice-automaton model, we simulate the effects of fiddler crabs on the distribution of excess Pb in marsh sediments. Three previously-identified modes of bioturbation are investigated: (1) removal-and-fill, where material is excavated to the sediment-water interface and burrows, when abandoned, are subsequently filled by surface material, (2) removal-and-collapse, where the infilling occurs by collapse of the burrow walls, and (3) partial-compaction-and-collapse, where part of the excavated sediment is packed into the burrow wall and abandoned burrows subsequently collapse. These various mixing modes lead to somewhat different laterally-integrated Pbex profiles, which are also influenced by burrowing frequency, burrow dimensions, fraction of surface material replaced by new sediment (regeneration), and the fraction of material compacted during burial. Using parameters from a previous study in a South Carolina marsh, we find that data from low-marsh sites are best predicted by the partial-compaction-and-collapse process; this is consistent with the observation that burrow casts indicate far more material is excavated than is deposited as pellets at the sediment-water interface. The profile from the high-marsh site is best simulated by removal-and-fill mixing, with 50% regeneration of material at the sediment-water interface; this is consistent with less frequent flooding at this site. We have also calculated the exchange function for each of these mixing modes and show that they are highly asymmetric, indicating that the mixing is not diffusive. Only in the case of partialcompaction-and-collapse does the exchange function approach a diffusive form when the excavation rate decreases, i.e., the probability of compaction increases.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".