Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Graph theory is a powerful body of mathematical knowledge, based on simple concepts, in which structural units are depicted as nodes with relationships between them depicted as lines. The nodes may have qualitative and quantitative characteristics, and the edges may have properties such as weights and directions. Graph theory provides a flexible conceptual model that can clarify the relationship between structures and processes, including the mechanisms of configuration effects and compositional differences. Graph concepts apply to many ecological and evolutionary phenomena, including interspecific associations, spatial structure, dispersal in landscapes, and relationships within metapopulations and metacommunities. We review applications of graph theory in biology, emphasizing graphs with spatial contexts. We show how spatial graph properties can be used for description and comparison as well as to test specific hypotheses. We suggest that future applications should include explicit spatial elements for landscape studies of ecological, genetic and epidemiological phenomena.
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.
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 | 0.000 |
| 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 it