BIOTURBATORS AS ECOSYSTEM ENGINEERS: ASSESSING CURRENT MODELS
Bibliographic record
Abstract
ABSTRACT Bioturbating organisms can dramatically alter the physical, chemical, and hydrological properties of the sediment and promote or hinder microbial growth. They are a classic example of “ecosystem engineers” as they alter the availability of resources to other species. Multiple evolutionary hypotheses evoke bioturbation as a possible driver for historical ecological change. To test these hypotheses, researchers need reliable and reproducible methods for estimating the impact of bioturbation in ancient environments. Early efforts to record and compare this impact through geologic time focused on the degree of bioturbation (e.g., bioturbation indices), the depth of bioturbation (e.g., bioturbation depth), or the structure of the infaunal community (e.g., tiering, ecospace utilization). Models which combine several parameters (e.g., functional groups, tier, motility, sediment interaction style) have been proposed and applied across the geological timescale in recent years. Here, we review all models that characterize the impact of bioturbators on the sedimentary environment (i.e., ‘ecosystem engineering'), in both modern and fossil sediments, and propose several questions. What are the assumptions of each approach? Are the current models appropriate for the metrics they wish to measure? Are they robust and reproducible? Our review highlights the nature of the sedimentary environment as an important parameter when characterizing ecosystem engineering intensity and outlines considerations for a best-practice model to measure the impact of bioturbation in geological datasets.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".