Productive capacity of an artificial stream in the Canadian Arctic: assessing the effectiveness of fish habitat compensation
Bibliographic record
Abstract
Few fish habitat compensation projects are assessed with respect to the principle of "no net loss" of productive capacity. Using reference streams as standards against which gains and losses of functions (e.g., production of fish) could be quantified, we examined the effectiveness of a 3.4-km artificial stream in the Northwest Territories, Canada. The artificial stream restored watershed connectivity, allowing fish migration and provided spawning and nursery habitat, particularly for Arctic grayling (Thymallus arcticus). However, the average mass of young-of-the-year (YOY) grayling at the end of summer was lower (57%) in the artificial stream than in natural streams. This difference in growth, in concert with estimates of grayling density, meant that the standing crop produced in the artificial stream averaged 37% of that found in natural streams. A bioenergetics model indicated that cooler water temperatures in the artificial stream had limited influence on growth. Instead, low amounts of autochthonous and allochthonous organic matter and poor physical habitat in the artificial stream appeared to limit the productivity of benthic invertebrates and fish. Our explicit analysis of productive capacity will allow future compensation measures to focus on deficiencies in the artificial stream and on the improvement of its productive capacity as fish habitat.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".