Computer modeling bioturbation: The creation of porous and permeable fluid-flow pathways
Bibliographic record
Abstract
Abstract Computer modeling of trace fossils (Skolithos, Thalassinoides, Planolites, Zoophycos, and Phycosiphon) and ichnofacies (Skolithos, Cruziana, and Zoophycos ichnofacies) is undertaken to assess the impact of bioturbation on porosity and permeability trends in sedimentary media. Model volumes are randomly populated with the digitally modeled trace fossils to test for connectivity between burrows. The probability of vertical and lateral interconnections is compared with bioturbation intensity. The results of the simulations indicate that biogenic flow networks develop at low bioturbation intensity, between 10 and 27.5% bioturbation (BI-2). However, the efficiency of connectivity is controlled by the architecture of the burrows. For all trace-fossil and ichnofacies models, regardless of trace-fossil orientation, continuous horizontal and vertical connectivity across the sediment volume is achieved within a 0 to 10% range in bioturbation. In subsurface aquifers and petroleum reservoirs, the presence of bioturbation can significantly influence fluid flow. In particular, for marine sedimentary rocks, where burrows are more permeable than the surrounding matrix, a greater degree of three-dimensional burrow connectivity can produce preferred fluid-flow pathways through the rock. Recognizing these flow conduits may enable optimization of resource exploitation or may contribute to increasing reserve estimates from previously interpreted nonreservoir rock.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".