Data and code for: New outdoor experimental river facility to study river dynamics (article submission)
Bibliographic record
Abstract
This dataset contains the data and code used to generate the figures and analyses for the manuscript “New Outdoor Experimental River Facility to Study River Dynamics”. The Outdoor Experimental River Facility (OERF) dataset includes (i) numerical compilations used for facility/natural-river comparisons, (ii) grain-size distributions and derived transport metrics, (iii) photogrammetric products (phase digital surface models) and derivatives used to quantify topographic change, (iv) velocity profile data from an acoustic Doppler velocimeter (ADV) near the inlet perturbation, and (v) text files that list source publications for the comparative facility figure. Folder structure mirrors manuscript figure numbering: • Fig01_outdoor_facility_publications – plain-text bibliographies used to compile published outdoor-facility examples. • Fig03_OERF_comparison – tabular datasets for natural rivers, indoor flumes, OERF envelope, and a MATLAB script to generate the synthetic OERF combinations. • Fig04_GSD – grain-size distribution (CDF/PDF) and derived series (Wilcock–Crowe transport per class, Rouse number). • Fig06_Physical_response – depth time series and digitized surface grain-size response across phases. • Fig07_DEM – phase digital surface models exported as .xyz (2 mm resolution; processed in PIX4D; 10 m flight altitude; GCP network measured with TS15 over GNSS-RTK benchmarks). • Fig08_velocity – ADV velocity profiles and summary streamwise statistics.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.202 | 0.162 |
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".