A 1‐D morphodynamic model of postglacial valley incision
Bibliographic record
Abstract
Abstract Chilliwack River is typical of many Cordilleran valley river systems that have undergone dramatic Holocene degradation of valley fills that built up over the course of Pleistocene glaciation. Downstream controls on base level, mainly blockage of valleys by glaciers, led to aggradation of significant glaciofluvial and glaciolacustrine valley fills and fan deposits, subsequently incised by fluvial action. Models of such large‐scale, long‐term degradation present a number of important challenges since the evolution of model parameters, such as the rate of bedload transport and grain size characteristics, are governed by the nature of the deposit. Sediment sampling in the Chilliwack Valley reveals a complex sequence of very coarse to fine textural modes. We present a 1‐D numerical morphodynamic model for the river‐floodplain system tailored to conditions in the valley. The model is adapted to dynamically adjust channel width to optimize sediment transporting capacity and to integrate relict valley fill material as the channel incises through valley deposits. Sensitivity to model parameters is studied using four principal criteria: profile concavity, rate of downstream grain size fining, bed surface sand content, and the timescale to equilibrium. Model results indicate that rates of abrasion and coarsening of the grain size distributions exert the strongest controls on all of the interrelated model performance criteria. While there are a number of difficulties in satisfying all model criteria simultaneously, results indicate that 1‐D models of valley bottom sedimentary systems can provide a suitable framework for integrating results from sediment budget studies and chronologies of sediment evacuation established from dating.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".