Towards interoperability and cooperation for the sustainable management of the St. Lawrence ecosystem
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Large amounts of data are regularly collected by various organizations carrying out their monitoring or research activities on the St. Lawrence ecosystem in response to a common need to better understand, model or predict changes that occur in the environment. However, access to such a wealth of information is often inefficient due to the lack of a common framework that ensures interconnections between organisations, data registries, systems and user interfaces, and the use of recognized standards. The vision behind the St. Lawrence Global Observatory (SLGO) initiative launched in 2005 is to provide efficient Web access to timely and accurate data and information from a network of federal, provincial, academic and community organizations for the sustainable management of the St. Lawrence ecosystem. The synergy created by clustering the means and expertise of the member organizations results in optimizing information dissemination, reducing duplicated efforts and identifying data gaps. It also helps support planning and decision making processes in areas such as public safety, climate change, resource management and conservation. This multidisciplinary and innovative approach is based on Web service development in a service-oriented architecture (SOA) and on access to distributed data assets including a broad range of real-time and archived data as well as modelling, forecasting and operational services. Pilot and demonstration projects lead by Fisheries and Oceans Canada (DFO) have allowed a team of programmers and scientists to develop the concept of Web Data Services (WDS), to implement several WDS and to successfully deploy Web-based client applications that exploit 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 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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".