Coarse particulate organic matter dynamics in small streams: towards linking function to physical structure
Bibliographic record
Abstract
Summary 1. Organic matter derived from terrestrial vegetation (detritus) is a key basal resource in many food webs, including those of streams. Once detritus enters a stream, it is either retained or transported, but the rates of within‐reach retention depend on structural characteristics of the stream channel, physical properties of the detrital particles and hydrology. Each of these attributes varies in time and space and the magnitude of variation may be affected by structural changes wrought by land‐use. We developed a heuristic model framework in an attempt to find a minimal model that captures the essential temporal dynamics of coarse particulate detritus in small streams. 2. The dynamics of our minimal model are driven by several frequently measured variables. Rate of litter breakdown, average transport distance of a particle, discharge and temperature are the primary variables. In addition, we tested the effects of altering assumptions about the nature of particle type and the effect of channel roughness on transport rates. 3. The model produced patterns of detrital standing stocks generally similar to those measured by empirical studies in small streams. Discharge‐dependent transport relations are relatively rare, but our assumed pattern of a sigmoid relationship between transport and discharge produced reasonable outputs. 4. We parameterised the model using data for small streams in coastal British Columbia, Canada. The outputs of the model indicate that channel roughness has a more profound effect on small, mobile particles, such as conifer needles, than on angiosperm leaves, such as alder. The model also indicates that the largest proportion of detritus is broken downin situin all model scenarios. 5. A major limitation to this model and the literature is a scarcity of detail of leaf interception and entrainment processes in streams. Progress towards integrating ecosystem functions and the dynamics of hydromorphology require integrating these physical drivers with biological processes. We present our model to emphasise that progress towards generalisable models of detrital dynamics is possible if links between physical structure and function can be made explicit.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".