Living Atlases Community
Bibliographic record
Abstract
Since 2010, the Atlas of Living Australia (ALA) provides information on all the known species in Australia and contributes to the Global Biodiversity Information Facility (GBIF). By lending access to this national open source platform, the open and modular architecture of ALA enables re-use of ALA tools by other countries and regions. Over the years, thanks to the ALA team and GBIF, the community has grown in different ways from production to training courses. Firstly, data portals based on ALA but residing outside Australia, have been launched in several institutions such as INBIO in Costa Rica and Canadensys in Canada, and in the GBIF network via at least six nodes presently operating national ALA-based portals (e.g., Spain, Portugal, France, Sweden, Argentina, United Kingdom). Others will follow in the coming years (e.g., Colombia, Peru). Other countries, such as Andorra and Benin, have also begun to develop their own installations with the aid of partners in the Living Atlases Community. Secondly, we are now able to set up workshops geared to different levels of expertise. At TDWG 2017 we will propose both beginner’s and advanced workshops. Thirdly, the experience gained by installing and customizing their own data portals has allowed many advanced participants to share their expertise in subjects like internationalization, data management, and customization, with others during workshops. Adding to these points, as an open source software, developers contribute to the community by implementing new functionalities and improving the translation into several languages for users of the software. Today, some modules are fully translated into Spanish, French, and Portuguese. In this poster, we will show the human aspect of the project by introducing the Living Atlases, an international community created around the ALA framework, highlighting how re-using existing software can be motivating and stimulating. We will also present the new official website that we will launch through the GBIF Capacity Enhancement Support Programme (CESP)*1 around the next advanced workshop and future projects planned in order to increase the durability of our community.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.233 | 0.122 |
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".