Statistical inference for food webs with emphasis on ecological networks via Bayesian melding
Bibliographic record
Abstract
Abstract Quantifying activities in a food web or ecological network, and related aspects of dependence, has largely been either descriptive or deterministic. Although schemes exist for assessing the reliability of such quantification, many are far from being statistical in nature. Statistical modeling approaches are explored, with a focus on the ecosystem aspects of a food web. By employing Bayesian melding, we provide a new statistical inferential approach for understanding ecological networks in the context of mass balance. Our approach embodies the traditional deterministic views on network relations, yet it is developed on the basis of proper statistical inference that allows the estimation of physical quantities and probabilistic assessment of the estimation. We describe our approach, and illustrate it with a mass balance dataset. The practical advantage of our approach is a more realistic understanding of the network by incorporating natural measurement variability into deterministic beliefs about the relationships among measurements. The resulting inference thus forms a more honest representation of the true state of nature, and provides a formal assessment of balance before data are passed on to later stages of an ecological network analysis (ENA). We also demonstrate that general Bayesian inference for ENA can yield new ecological insight that may not be available through standard classical inference. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.019 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".