Estimating terrestrial contribution to stream invertebrates and periphyton using a gradient‐based mixing model for δ<sup>13</sup>C
Bibliographic record
Abstract
Summary 1. This paper outlines a gradient‐based model that can be used for isotopic signature source partitioning, even if source signatures are not distinct, as long as their spatial gradients differ. A model of this type is applied to the partitioning of autochthonous vs. allochthonous contribution to stream invertebrate δ13C signatures, which has often been confounded by overlap in source signatures. 2. δ13C signatures of inorganic carbon and most autochthonous production exhibit pronounced gradients along rivers, being depleted relative to terrestrial signatures in upstream reaches, and enriched downstream. Terrestrial detritus, by contrast, exhibits no gradient. Thus terrestrial food consumption reduces downstream signature slopes in proportion to the amount of terrestrial food consumed. 3. The gradient‐based mixing model produces estimates of the proportion of terrestrial consumption (pT) from signature slopes of consumers; pT estimates for invertebrate primary consumers were: herbivore/grazers (0·15) 4. Periphyton (epilithon), a mixture of attached algae, bacteria and detritus, exhibited a weaker downstream slope than attached algae. pT values calculated for periphyton relative to pure algal signatures were 0·32 implying ∼30% allochthonous content. The slope for herbivore/grazers calculated relative to periphyton signatures was >1, indicating selective assimilation of the autochthonous component from the biofilms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".