Applying a method before its proof of concept: A cautionary tale using inferred food webs
Bibliographic record
Abstract
There are significant pitfalls associated with developing food webs using inferred approaches, including violations of ecological assumptions, which considerably undermine their potentiality to resemble ecological communities and hence be practically useful. As data‐driven scientists, we must, at the very least, test against some empirical data to establish confidence that indeed the inferred food webs reflect their supposed in situ communities. Otherwise, using inferred networks to make bold claims—like the titular statement by Botella et al. (2024) that food webs are influenced by land‐use intensity—is highly unlikely to be useful, especially when the aforementioned claim is unsubstantiated by the Authors' own statistical analyses.
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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 | 0.251 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.018 | 0.053 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".