BioBeacon: an Online Field Guide to Digital Biodiversity Information Resources
Bibliographic record
Abstract
Managing the biodiversity crisis requires access to credible information on species, as well as their changing abundance and spatio-temporal distributions, among other variables. Technological advances are expanding both the variety and volume of data available, resulting in the emergence of biodiversity informatics as a rapidly growing research paradigm. Many online resources exist, such as GBIF's resources and tools page (Anonymous 2017), however the lack of fundamental categorization inhibits efficient location and use of relevant data for biological research, conservation, education and industrial application. BioBeacon is a student-driven collaboration between the Biodiversity major at University of Guelph and the Biodiversity Institute of Ontario. Its purpose is to shine a light on biodiversity information resources and characterize them according to objective criteria that simplify their navigation and increase accessibility. Criteria will include several categories such as data type, source, region of focus, and current status, as well as many tags for more refined searches. The refined search feature and categorization of databases will be the primary distinguishing charactersitics that separate BioBeacon from previous biodiversity database indexing efforts. We envision BioBeacon to be cooperatively managed by its creators and steering committee, while inviting input from stakeholders and other parties of interest. Ideally, BioBeacon will grow to incorporate relevant biodiversity information resources that bear diverse types of data from locations around the world.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.353 | 0.317 |
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".