Reaching an Established but Growing Network: Use-case from Canadensys
Bibliographic record
Abstract
Canadensys is an associate GBIF node in Canada, officially established as a node in 2014, but publishing data on GBIF since 2011. Since then, Canadensys has grown from nine institutions to a network of nearly 25 institutions that publish biodiversity data and we have migrated from an in-house explorer, to a Living Atlases (LA) framework. Canadensys publishes data curated or collected by Canadian universities, museums, as well as municipalities and non govermental organizations (NGOs). Establishing a new network can be challenging, but several resources and programs exist to help node managers and node participants initiate the publication process. Keeping an established network alive while continuing to grow and to develop new methods and technologies is also an important challenge, especially in a context where institutions are geographically separated across large distances, and where funds are scarce or mostly oriented towards highly innovative projects. With the aim to reach both established and new participants across Canada and from adjacent regions in the USA, and in order to help them to familiarize themselves with the new framework based on LA, we organized three workshops on data publication and data usage. Partially funded through a GBIF CESP project, this series of workshops was developed in partnership with international, regional and national partners such as iDigBio, OBIS Canada and GBIF Spain. The workshops helped new participants prepare and publish data, and allowed established publishers to enrich and update their resources on Canadensys and GBIF. The project also highlighted some of the challenges our network is facing, such as funding, infrastructure, human resources, and communication. Feedback from participants shows that the workshops were successfull in terms of capacity enhancement, giving knowledge and tools to data manager in order to prepare and publish standardize data, as well as to transfer that knowledge in their respective institutions. All materials and documentation developed during this project will be made available on Canadensys, allowing everyone interested to follow the curriculum. Sharing our experience will be useful for other nodes wanting to introduce the LA framework to their users and to enhance capacities in the network.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".