Estimating trophic position in marine and estuarine food webs
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Bibliographic record
Abstract
Structural or binary approaches, based on presence‐absence of feeding links, are the most common method of assembling food webs and form the basis of the most well explored food web models. Binary approaches to assembling feeding links are often criticized as being less powerful and accurate than flow‐based methods. To test this assumption we compared binary estimates of trophic position with estimates based on stable isotope values of nitrogen (δ 15 N). For 366 species from eight marine and estuarine food webs we compared trophic position estimates based on binary (presence‐absence) feeding links with estimates based on the stable isotope of nitrogen (δ 15 N). For a subset of 127 fish species in four of the webs we further compared trophic position estimates based on gut content analysis using a flow‐based algorithm using data from FishBase.org with binary and δ 15 N estimates. Across all species and webs binary estimates of trophic position were strongly correlated (R = 0.644) with δ 15 N estimates. On average binary estimates differed from baseline corrected δ 15 N estimates by 2.33% for mean trophic position and 6.57% for maximum trophic position. On average the difference between binary δ 15 N estimates was 0.14 of a trophic level. For the subset of 127 fish species binary estimates performed similarly or more accurately in predicting δ 15 N values than the flow‐based estimates. Binary approaches to assembling feeding links are often criticized as being less powerful and accurate than flow‐based methods. Our results show a high concordance between binary and δ 15 N estimates of trophic position as well as showing that in some cases binary estimates are better predictors of δ 15 N than flow‐based estimates, reaffirming the robustness of the structural approach to assembling food webs. Additional cross‐validation studies in other ecosystems are necessary to determine whether our results can be generalized to terrestrial and freshwater ecosystems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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