Corvid Cleaning: Deploys Crows to Clean Up Cigarette Butts in Sweden
Bibliographic record
Abstract
The carelessness of smokers has made cigarette butts the most pervasive tobacco product waste on beaches and on streets in urban areas. Cigarette butts may be small in size, but the havoc they wreak on human health and the natural environment is significant. Corvid Cleaning is a unique learning organization that came up with the idea of using crows and other corvids to clean cigarette butt waste. Christian Gunther-Hanssen, the founder of Corvid Cleaning, is faced with a unique challenge. While studying at Lund University in 2013, he envisioned using crows to address the pervasive problem of cigarette butt pollution in urban areas. Now, based in Södertälje, near Stockholm, Christian is on the verge of launching a pilot programme to deploy trained crows for this purpose. Christian’s decision dilemma revolves around determining the most effective way to implement and scale this innovative solution. The primary options include focusing on a small-scale pilot programme in Södertälje to refine the process or seeking broader support to quickly expand the initiative to other cities. Additionally, Christian must consider the ethical implications and potential health impacts on the crows involved. These choices will shape the future of Corvid Cleaning and its potential to revolutionize urban litter management. The case also highlights the inception of the project, its timeline, the process for training corvids, an examination of Corvid Cleaning from the context of the 4A framework, operational sustainability and, finally, the future of the project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".