Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".