Bibliographic record
Abstract
Under the Fisheries Act, highway construction projects that result in harmful disruption or destruction of fish habitat require replacement habitat. At Yukon Highways & Public Works we have developed successful compensation projects together with the Department of Fisheries and Oceans, and in many cases with the help of consultants. This paper describes three Yukon examples. The first project describes the re-establishment of fish passage through two large culverts that were elevated. This was done back by backwatering the culvert inlets, constructing a riffle, and by installing baffles in one of the culverts. For the second project we will be creating over-wintering habitat for grayling in a small fresh water stream by excavating small side channels. The last project describes how we will be connecting two old gravel pit lakes with a large glacially fed river system. This will provide year-round fish habitat and a refuge for fish in summer to escape from turbid river water. 1
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".