Canadensys and GBIF France: collaboration through the GBIF network to help launch the new Canadensys explorer
Bibliographic record
Abstract
With over 780 million records, the Global Biodiversity Information Facility (GBIF) is an international consortium whose goal is to collect data on biodiversity and make them available, free and online, to everyone. Canadensys (http://www.canadensys.net/) is a network of researchers, collectors, curators, information technologists, students, and educators that shares data on the occurrence and taxonomic identity of plants, animals and fungi in Canada. It is an associate participant of GBIF. The French node of GBIF, GBIF France (http://www.gbif.fr/), among other objectives, provides and helps French data publishers to connect their data through the GBIF network. Since 2014, GBIF France's technical team has installed and configured their data portal using Atlas of Living Australia (ALA) tools. One of the main missions of GBIF is to assist nodes in creating global and regional collaborations in order to transfer knowledge on tools and methods which help providers enhance their data and upload them into the GBIF network. To achieve this, GBIF initiated the Capacity Enhancement Support Program (CESP) (http://www.gbif.org/programme/capacity-support). The mentoring activity presented in this poster is one of the five types of activities supported by CESP. The amount of data served through Canadensys has increased dramatically during the past years. The consequence is that its current system is not sufficiently powerful to effectively treat this increased volume of data. In order to meet this requirement, Canadensys has begun to configure and install its system based on the ALA framework. Through this CESP mentoring activity, GBIF France is helping Canadensys launch its own data portal by providing assistance on technical specifications, such as server configuration and customization. In addition to this main objective, GBIF France and Canadensys aim to extend the ALA community and include French-speaking countries (especially in Africa and the Caribbean). In fact, projects with the aforementioned countries have already been proposed through CESP and other calls. Efforts to improve the ALA integration documentation and provide French translations are underway. In this poster, Canadensys presents its new system and its functionalities. We also describe the collaboration between GBIF France and Canadensys and the improvements made on the documentation, which will facilitate installation of national or regional data portals in French-speaking countries.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".