Bibliographic record
Abstract
Species' evolutionary histories shape their present-day ecologies, but the integration of phylogenetic approaches in ecology has had a contentious history. The field of ecophylogenetics promised to reveal the process of community assembly from simple indices of phylogenetic pairwise distances - communities shaped by environmental filtering were composed of closely related species, whereas communities shaped by competition were composed of less closely related species. However, the mapping of ecology onto phylogeny proved to be not so straightforward, and the field remains mired in controversy. Nonetheless, ecophylogenetic methods provided important advances across ecology. For example the phylogenetic distances between species is a strong predictor of pest and pathogen sharing, and can thus inform models of species invasion, coexistence and the disease dilution/amplification effect of biodiversity. The phylogenetic structure of communities may also provide information on niche space occupancy, helping interpret patterns of facilitation, succession and ecosystem functioning - with relevance for conservation and restoration - and the dynamics among species within foodwebs and metacommunities. I suggest leveraging advances in our understanding of the process of evolution on phylogenetic trees would allow the field to progress further, while maintaining the essence of the original vision that proved so seductive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".