The use of DNA barcodes in food web construction—terrestrial and aquatic ecologists unite!
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
By depicting who eats whom, food webs offer descriptions of how groupings in nature (typically species or populations) are linked to each other. For asking questions on how food webs are built and work, we need descriptions of food webs at different levels of resolution. DNA techniques provide opportunities for highly resolved webs. In this paper, we offer an exposé of how DNA-based techniques, and DNA barcodes in particular, have recently been used to construct food web structure in both terrestrial and aquatic systems. We highlight how such techniques can be applied to simultaneously improve the taxonomic resolution of the nodes of the web (i.e., the species), and the links between them (i.e., who eats whom). We end by proposing how DNA barcodes and DNA information may allow new approaches to the construction of larger interaction webs, and overcome some hurdles to achieving adequate sample size. Most importantly, we propose that the joint adoption and development of these techniques may serve to unite approaches to food web studies in aquatic and terrestrial systems-revealing the extent to which food webs in these environments are structured similarly to or differently from each other, and how they are linked by dispersal.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it