Bibliographic record
Abstract
Biodiversité Québec is an integrated scientific monitoring system. It allows better observation, analysis and sharing of the state and changes of biodiversity for better management and conservation of ecosystems in Québec. The strength and innovation of this infrastructure depend on the collective effort to gather information on biodiversity. Its Atlas data infrastructure aims to integrate and provide access to biodiversity data for the Province of Quebec. It standardizes various data types (abundance, occurrences, surveys, population time-series, and taxonomy) for integration into monitoring and modeling workflows. Data is sourced from open science repositories (e.g., GBIF, eBird, iNaturalist, Living Planet Database) and through direct partnerships with local and national organizations. All occurences covering the province of Quebec within GBIF are downloaded via API and then filtered to select only those with complete datetime, location, and taxonomy information. These occurrences are then transformed to fit the BQ Atlas observations data model, preserving dataset attributes such as publishers, licenses, and DOIs for citation purposes, as well as maintaining original identifiers from GBIF and the source datasets. This data is stored on on-premises relational database servers and made available for exploration and download through the Atlas data portal and R packages.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.024 |
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".