Loop analysis quantifying important species in a marine food web
Bibliographic record
Abstract
Improving the predictive power of food web analysis is a major challenge. Identifying the relationships that link\ntopological and dynamical features may help. We used the predictions of loop analysis about the effect of perturbations targeted to the components of Barents sea food web to quantify their sensitivity and community\nimpact, that we summarized in two new indices, NI and NS. Using a multivariate analysis we interpreted the\nmeaning of these indices in a benchmarking exercise using several well recognized indices of species topological\n(positional) importance. Our findings suggest that the information the two indices proposed here provides does\nnot overlap with that of more diffused topological indices of positional importance (i.e. centrality indices). The\nformer are express the dynamic consequences of the topology in which species are embedded, whereas for the\nlatter such dynamical consequences are mostly hypothesized on a topological base. The indices of loop analysis\nare based on the effective role a species plays in passing the impacts to other species (NI) and their role as sinks of\nthe perturbations entering anywhere in the system (NS). These two indices, in the end, reveal how the topology\nof the network affects the response of the species to perturbations and thus emphasize the interaction between\ntopology and dynamics. Based on our results, the question related to conservation is whether to prioritize sensitive species, that can be more strongly influenced when others are perturbed, or species of high impact, that can\nmore strongly influence the rest of the community if perturbed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".