Robust model-based indicators of regional differences in food-web structure in the Southern Ocean
Bibliographic record
Abstract
Efforts to model marine food-webs are generally undertaken by small teams working separately on specific regions (<106 km2) and making independent decisions about how to deal with data gaps and uncertainties. Differences in these largely arbitrary decisions (which we call ‘model personality’) can potentially obscure true differences between regional food-webs or lead to spurious differences. Here we explore the influence of model personality on a comparison of four Southern Ocean regional food-web models. We construct alternative model versions which sequentially remove aspects of personality (alternative model ‘currencies’, schemes for aggregating organisms into functional groups, and energetic parameter values). These alternative versions preserve regional differences in biomass and feeding relationships. Variation in a set of model metrics that are insensitive to absolute biomass and production identifies multiple regional contrasts, a subset of which are robust to differences in model personality. These contrasts imply real differences in ecosystem structure which, in conjunction with differences in primary production and consumer biomass (spanning two and four orders of magnitude respectively), underpin differences in function. Existing regional models are therefore a useful resource for comparing ecosystem structure, function and response to change if comparative studies assess and report the influence of model personality.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".