Bibliographic record
Abstract
Abstract.—Assessments of stream fish or benthos assemblages normally involve a contrast of conditions at “test ” sites to conditions represented by “regional ” reference sites that are either minimally or least disturbed. Identification of reference sites is difficult and normally involves a variety of subjective criteria. The development of reference models for stream fish and benthos in the Canadian tributaries of Lake Ontario is particularly challenging because there are few undeveloped areas and there is no consensus on criteria for a least-disturbed condition. Rather than identify sites as representing a least-disturbed condition, we developed a series of models that relate the existing biophysical condition of streams (i.e., the fish, benthos, and instream habitat) to landscape (i.e., slope, geology, catchment area) and land use/land cover (percent impervious cover [PIC]). Relationships between indices of biophysical condition and PIC can be used to “hindcast ” or estimate the expected biophysical condition at a variety of land cover scenarios. The models cannot be used to predict conditions outside the calibration data range, but this approach does allow us to make use of a disturbance gradient and make predictions with a minimal number of least-disturbed sites. The difference between the hindcast reference and present day conditions is an estimate of present-day impacts. Results from this exercise provided an estimate of the magnitude of impairment of streams in the Canadian portion of the Lake Ontario region.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".