Pilotstudie inzet Litter-ID bij de landelijke monitoringstrategie voor rivierafval : Resultaten van een pilotstudie waarin onderzocht is of en hoe de Litter-ID-methodiek kan bijdragen aan de landelijke monitoringstrategie voor rivierafval van Rijkswaterstaat
Bibliographic record
Abstract
In dit rapport staan de resultaten en aanbevelingen van een pilotstudie waarin in opdracht van Rijkswaterstaat Water, Verkeer en Leefomgeving (namens het Ministerie van Infrastructuur en Waterstaatstaat) bepaald is of en hoe de door Wageningen University & Research ontwikkelde Litter-ID-methodiek kan bijdragen aan de landelijke monitoringstrategie van rivierafval, die ontwikkeld wordt vanuit Rijkswaterstaat.---This report contains the results and recommendations of a pilot project commissioned by Rijkswaterstaat Water, Traffic and the Environment (on behalf of the Ministry of Infrastructure and Water) to determine if and how the Litter-ID methodology, developed by Wageningen University & Research, can contribute to the national monitoring strategy for riverine litter currently being developed by Rijkswaterstaat.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".