Future-proofing environmental DNA and trait-based predictions of food webs
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
Abstract Food webs represent trophic interactions within ecosystems. Matching traits of consumers and resources helps infer trophic interactions and food-web properties. Environmental (e)DNA, commonly used for detecting species occurrences, is rarely used in trait-matching studies because abundance estimates and descriptions of relevant traits are generally missing. We synthesized recent literature on inferences of trophic interactions with eDNA and trait matching to identify challenges and opportunities for coupled eDNA–trait recording schemes. Our case study shows how coupling eDNA and trait data collection improves the ability to characterize greater numbers of food webs across multiple scales ranging from spatiotemporal to trait variation. Future-proofing eDNA data sets requires the collection of new traits or the compilation of existing trait data at spatiotemporal scales that are relevant to detect current and future changes in food webs and ecosystems.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it