Canadian Museum of Nature Hudson Bay Lowlands Project
Bibliographic record
Abstract
The Hudson Bay Lowlands Project includes data about Canadian Museum of Nature specimens collected in the Hudson Bay Lowlands geographical location, spanning several CMN datasets. The larger goal of the project is to determine the state of knowledge of biodiversity in the Hudson Bay-James Bay ecoregion and to use the available data to conduct further research into carbon sequestration, biodiversity modeling, etc. This data set was produced and is maintained to support access to historic biodiversity information from the region.This page is a metadata-only resource that serves as the central project page for the Hudson Bay Lowlands Project for the Canadian Museum of Nature. This resource page does not contain any records of its own. Instead, this resource contains all relevant information and metadata about the Hudson Bay Lowlands Project, a link to the records in the form of a search on GBIF from multiple CMN datasets, and a regularly updated DOI link to download all records related to the project across all CMN datasets (found below). The following search link comprises CMN records from multiple datasets where datasetName contains the project tag "Hudson Bay Lowlands Project": https://www.gbif.org/occurrence/search?advanced=1&institution_key=c146edb6-3a82-473c-b337-4acaa92f9513&dataset_name=Hudson%20Bay%20Lowlands%20Project The following download links are available in Simple CSV and DWCA formats. For more information on GBIF API occurrence download formats, see the documentation at https://techdocs.gbif.org/en/openapi/v1/occurrence. Downloads as of 2026-01-16: - Darwin Core Archive: https://doi.org/10.15468/dl.823tnt - Simple CSV (tab delimited): https://doi.org/10.15468/dl.32ruyy
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.175 | 0.062 |
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".