Community divergence and convergence along experimental gradients of stress and disturbance
Bibliographic record
Abstract
We created 24 mesocosms containing mixtures of herbaceous species arranged along experimentally maintained gradients of stress (external abiotic constraints limiting biomass production) and disturbance (events causing partial or total live biomass destruction) in order to determine the degree to which community assembly is deterministic or historically contingent during succession. In this 7-yr experiment, we found taxonomic divergence and functional convergence during the last 3 yr. Although communities became more functionally dissimilar as the difference in the level of stress increased, they were equally taxonomically different irrespective of the amount of difference between them in terms of stress and disturbance. In addition, comparing communities experiencing the same conditions, taxonomic community structure was more dissimilar as the levels of stress and disturbance decreased. Therefore, community assembly was largely deterministic from a functional perspective but more historically contingent from a taxonomic perspective, and the relative importance of taxonomic historical contingency decreased as the levels of stress and disturbance frequency increased.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".