The ivory poaching crisis: Which way forward?
Bibliographic record
Abstract
In 2017, it was estimated that there are less than a quarter of the expected number of elephants in Africa’s protected areas, yet poaching for ivory is a persisting pandemic. I picked up this piece of ivory, that had naturally chipped off the tip of an elephant tusk, while doing fieldwork in Botswana. In monetary value, I would guess that it was worth about U$500, but to some the value of this piece of ivory could falsely mean life or death. Luckily, it seems that the use of ivory for medicinal and recreational purposes has become less popular as more countries ban the use and trade of ivory. My PhD research focusses on finding long term conservation solutions for elephants in southern Africa. I use genetic and ecological methods in an integrative way to find conservation strategies that could hopefully assuage some the negative consequences of poaching. After photographing this piece of ivory, I threw it as far back into the bush as I could, nobody in this world needs an elephant tusk but an elephant. Camera: Canon EOS 450D, lens Canon EFS 18-55mm.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".