Mining Biodiversity - Project Plan
Bibliographic record
Abstract
The Mining Biodiversity project is an international collaboration between the National Centre for Text Mining (UK), Missouri Botanical Garden (US) and Dalhousie University’s Big Data Analytics Institute and Social Medial Lab (Canada). Its overarching goal is to transform the Biodiversity Heritage Library (BHL), a digital library of over 40 million pages of taxonomic literature, into a next-generation social digital resource to facilitate the collaborative study and discussion of legacy biodiversity documents by a worldwide community. Furthermore, it aims to raise awareness of the changes in biodiversity over time in the general public. As the BHL holds an immense amount of biodiversity documents, the project will leverage text mining and visualisation methods, crowdsourcing and social media to effectively serve its users with semantically enriched content. The resulting digital resource will provide fully interlinked and indexed access to the full content of BHL library documents, via semantically enhanced, interactive browsing and searching capabilities, allowing users to precisely and conveniently locate the information of interest to them.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".