Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This dataset includes information about valley segment and catchment summaries, valley characteristics, instream habitat, and fish for valley segments, sites, and transects along four river mainstems in Ontario, Canada. Moving west to east, the rivers include the Grand River which ends in Lake Erie at Port Maitland, the Ganaraska River which ends in Lake Ontario at Port Hope, the Trent River which ends in the Bay of Quinte at Trenton, and the Petawawa River which ends in the Ottawa River at Petawawa. These rivers vary in natural character, anthropogenic development, and fish assemblages. Riverine sites along the mainstems of all four rivers included a total of one hundred and twelve sites. Sampling on the Grand, Trent, and Petawawa Rivers focused on non-wadeable lower river mainstems, whereas all sites on the Ganaraska River mainstem were wadeable and incorporated a wider range of stream sizes. Sites were sampled between 1997 and 2001, with many sites sampled in multiple years. The study design for the Grand, Trent, and Petawawa Rivers include a hierarchical design where data collection was nested at three spatial scales -- shoreline and channel transect data are nested within sites, and sites are nested within valley segments. In the Ganaraska River, data collection was by site and nested within valley segments. A complementary set of shapefiles for each river supports these tabular data and provides items needed to map watersheds, valley segments, and sites, and to calculate additional variables for sites and site catchments. The metadata specific to these spatial data is associated with the shapefiles and is not described here.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it