The Real-World Impacts of Woodcutting in Old School RuneScape
Bibliographic record
Abstract
This paper looks at the process of achieving a maximum woodcutting level (99) within the game Old School RuneScape (OSRS) and looks at the potential effects if these actions occurred in real life. An assumption made is that only teak trees are cut, as this is the most prevalent type of tree cut within the game while levelling up. The value obtained is 153,082 teak logs per player. Then the conversion between logs obtained in the game to real-life trees is calculated to be 8 logs for each real-life tree. Using real-world values from teak farms, it is found that 172,224 m 2 of space and 19,136 teak trees are needed for one player to achieve level 99. The potential consequences of these actions are discussed in the case that every single account with level 99 woodcutting within OSRS completed a similar process in real life. The potential result is that 14.7% of the world’s teak farms would need to be cut and the carbon storage of these trees can be compared to the addition of 1,009,200 cars over 10 years, approximately 3.2% of the total cars in the UK.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".