Rise of the Eco Warriors
Bibliographic record
Abstract
A group of passionate and naïve young people leave their known worlds behind to spend 100 days in the jungles of Borneo. Their mission is to confront one of the great global challenges of our time, saving rainforests and giving hope to endangered orangutans. Their task is enormous and the odds are against them. Jojo, an orphaned baby orangutan, is entrusted in their care and they must find a way to return her to her forest home. To do this, they need to build an orangutan rehabilitation centre and find ways to help the local communities protect their forest. Under the guidance of their mentor Dr Willie Smits, they introduce an innovative satellite monitoring system called Earthwatchers and enlist the help of school students around the world. The system is put to the test when the bulldozers move in and threaten the future of a nearby community living in a traditional longhouse. This is a story about what it takes it be an eco-warrior, an individual willing to step up and take action to avert a global catastrophe taking place before our eyes. The eco-warriors represent a new generation, ready to face what is happening on our planet and willing to do something, no matter how small, to build a more humane and balanced world. For them, every individual matters, every action counts. - Written by Cathy Henkel
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".