Cardiac sarcoidosis: applications of imaging in diagnosis and directing treatment
Bibliographic record
Abstract
Abstract Environmental disturbances have long been theorized to shape the diversity and composition of ecosystems. However, fundamental limitations in our ability to specify the scale and features of a disturbance in the field and laboratory have produced an inconsistent picture of diversity-disturbance relationships (DDRs). Using a recently developed automated continuous culture system, we decomposed a dilution disturbance into intensity and fluctuation components, and tested their effects on diversity of a soil-derived bacterial community across hundreds of replicate cultures. We observed an unexpected U-shaped relationship between diversity and disturbance intensity in the absence of fluctuations, counter to classic intuition. Adding fluctuations erased the U-shape and increased community diversity across all disturbance intensities. All of these results are well-captured by a Monod consumer resource model, and can be explained by a novel “niche flip” mechanism wherein tradeoffs between species growth parameters produce coexistence regimes that collapse at intermediate disturbance levels. Our results illustrate that compositional complexity of an ecosystem can be generated and predictably reshaped using temporal environmental patterns, and highlight how distinct features of disturbance can interact in complex ways to govern ecosystem assembly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".