The structure of probabilistic networks
Bibliographic record
Abstract
Summary There is a growing realization among community ecologists that interactions between species vary across space and time and that this variation needs to be quantified. Our current numerical framework to analyse the structure of species interactions, based on graph‐theoretical approaches, usually do not consider the variability of interactions. As this variability has been show to hold valuable ecological information, there is a need to adapt the current measures of network structure so that they can exploit it. We present analytical expressions of key measures of network structured, adapted so that they account for the variability of ecological interactions. We do so by modelling each interaction as a Bernoulli event; using basic calculus allows expressing the expected value, and when mathematically tractable, its variance. When applied to non‐probabilistic data, the measures we present give the same results as their non‐probabilistic formulations, meaning that they can be generally applied. We present three case studies that highlight how these measures can be used, in re‐analysing data that experimentally measured the variability of interactions, to alleviate the computational demands of permutation‐based approaches, and to use the frequency at which interactions are observed over several locations to infer the structure of local networks. We provide a free and open‐source implementation of these measures. We discuss how both sampling and data representation of ecological networks can be adapted to allow the application of a fully probabilistic numerical network approach.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".