The accuracy of a river bed moulding/casting system and the effectiveness of a low‐cost digital camera for recording river bed fabric
Bibliographic record
Abstract
Abstract Digital photogrammetry has been used to develop and test an artificial river bed moulding and casting system, which allows the pebbles within a coarse‐grain river bed to be recreated for hydraulic research in a laboratory flow channel or flume. Imagery of both the original streambed and the cast facsimile was acquired using a non‐metric Kodak DCS460 digital camera and digital elevation models and orthophotographs were derived and compared to assess the accuracy of the moulding and casting system. These comparative tests proved to be critical in modifying and developing the system. Additional imagery was obtained in the field using a non‐metric Olympus C3030 ‘‘compact’’ digital camera to assess whether far cheaper camera technology could deliver data appropriate for such comparative examinations. Internal calibration parameter sets and data that were generated were compared with data obtained by the non‐metric Kodak DCS460. These tests demonstrate that digital sensors built around high‐quality 35 mm professional camera bodies and lenses are required for comparative examinations and for similar system development.
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".