Modelling Recovery Rules Of Soil Microbial Assemblages UsingMatrix Methods
Bibliographic record
Abstract
We model the dynamics of microbial assemblages in response to various rehabilitation treatments to polluted soils in the Sudbury, Canada area. We compare the efficacy of the stationary Markov model in capturing observed dynamics and measuring the success of rehabilitation techniques. We apply two different approaches to the estimation of transition probabilities for the Markov model. The first method models competition between taxa for a limiting resource. The second method is less mechanistic, however, and is closely linked to the matrix method of Principal Components Analysis that is widely used to detect ecological gradients. The first method provides a better fit to the dynamics of microbial assemblages and to assessment of rehabilitation success, but is less stable in the prediction of final states. The second method provides a generally poor fit to assemblage dynamics, but a more accurate prediction of final state. We emphasize the importance of defining criteria for model assessment and how these may depend intrinsically on model construction.
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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 | 0.001 | 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 | 0.001 |
| 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".