The Detri<sup>2</sup>match conceptual framework: Matching detritivore and detritus traits to unravel consumption rules in a context of decomposition
Bibliographic record
Abstract
Abstract From soil to freshwater ecosystems, decomposition can be conceived as the result of interactions between organic matter and a diversity of organisms. This function is driven in part by detritivores, invertebrates that feed on detritus or graze on its associated microbes and that have a significant but extremely variable contribution to decomposition. In order to better understand and predict detritivore–detritus pairwise interactions, we propose a conceptual framework, called Detri2match, to study the consumption of detritus by detritivores, using a trait‐matching approach at the individual detritivore level. Here, we focus on the interaction between saprophagous detritivores that fragment plant detritus. We propose a novel definition of a saprophagous detritivore as an animal that consumes plant detritus when its traits match sufficiently the traits of its resource, passing through five interaction facets of consumption. These include (1) a spatial match rule regarding the encounter, (2) a biomechanical match rule regarding ingestion, (3) a digestive match rule regarding assimilation, (4) an energetic match rule regarding the fulfilment of metabolic needs and (5) a nutritional match rule regarding the fulfilment of chemical element needs in adapted proportions. The main goal of this framework is to guide future research to establish generic rules of misunderstood detritus–detritivore pairwise interactions by identifying relevant interaction facets and their key associated traits for both detritivores and detritus. This investigation should be conducted over the temporal variability of trait‐matching constraints throughout the whole decomposition process. Coupled with adequate accumulation of trait information, the Detri2match framework could also facilitate predictions by inference of non‐tested pairwise detritivore–litter interactions. We also outline conceptual, methodological and analytical challenges of this framework. The main challenge would be to scale up these pairwise rules at the detrital network level and to test their genericity, which would contribute to a better understanding of the functioning of the detrital network and its contribution to decomposition. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".