Sponge Spicules, Silicification, and Sequence Stratigraphy
Bibliographic record
Abstract
Abstract: Early diagenetic silica is distributed unevenly in Phanerozoic sponge-spicule-rich carbonate mounds. In Paleozoic mounds, replacive silica occurs principally at the microscopic scale, whereas in Mesozoic strata, it is frequently present as nodular chert. This paper explores the possibility of advective–dispersive loss of dissolved pore-water silica for explaining the contrasting styles of silica cycling in mounds. Four examples of spicule-rich mounds were transcribed into hydrogeological conceptual models: two weakly silicified Paleozoic mounds with stromatactis (Silurian, Carboniferous), and two strongly silicified Jurassic mounds (one stromatactis mound, one sponge mound). Monte-Carlo fluid flow simulations utilized literature-derived ranges of effective porosity while hydraulic conductivity of component hydrostratigraphic units provided quantitative constraints. For the Paleozoic examples, the strata aggrade and prograde, which creates shallowing-upward trends. Mesozoic examples aggrade and retrograde, resulting in deepening-upward trends, and stratigraphic condensation and fluid baffling (interpreted from hardgrounds and black shale). For the Silurian and the Jurassic stromatactis-mound models, simulations of advective–dispersive silica mass transport were performed by applying different compaction-driven fluid fluxes (based on stratigraphic data). The Silurian mound displays vertical flushing, but high silica concentrations prevail in off-mound and flank deposits. The Jurassic case maintains a stratiform-like silica distribution over tens of thousands of years. The two other cases (Carboniferous, Jurassic) used an identical low fluid flux. The simulations which match field observations (silica-depleted mound versus stratiform chert) occur at different time scales, realistic for the Jurassic case but unrealistic for the Carboniferous case because it lacks the required degree of stratigraphic condensation to establish a silica-depleted mound. The results suggest that the variation of fluxes of dissolved pore-water silica in Phanerozoic spicule-rich mounds is controlled by mass accumulation or sediment burial rate. During the Paleozoic, mound formation is commonly associated with shallowing-upward trends (high to intermediate accumulation rates), whereas Mesozoic mound formation more commonly extended into condensed sections with low mass accumulation and respective low rates of compaction-driven fluid flow. The Mesozoic extension of spicule-rich carbonate mounds into the condensed section might be related to an intensification of benthic–pelagic coupling established during the Mesozoic marine revolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".