A new model for ecological networks using species‐level traits
Bibliographic record
Abstract
Summary Recent studies on plant–pollinator networks have focused on explaining network structure through linkage rules, including spatio‐temporal overlap, and phenotypic trait or phylogenetic signal complementarity. Few studies, however, have quantified the extent to which functional traits affect the probability of plants and pollinators interacting with each other. Dirichlet‐multinomial (DM) regression is a consumer‐resource model for the interaction probabilities in a mutualistic network. This flexible model accommodates network heterogeneity through random effects and overdispersion and can estimate the contribution of species‐level traits to plant–pollinator interactions. Using artificial networks based on linkage rules and neutrality, we evaluate the performance of DM regression and explore the model's parameter space. We also analyse an empirical network in which the interaction probabilities are modelled by species characteristics. Study results show that such random effects models can provide good fits to observed data. The characteristics pollinators seek in plant species may be better anticipated if species interactions are modelled by the functional traits that drive them.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".