Two analytical approaches for quantifying physical habitat as a limit to aquatic ecosystems
Bibliographic record
Abstract
An early activity in any environmental flow study is the identification of factors limiting both human and environmental benefits derived from the aquatic ecosystem. Limiting factors include (1) physical habitat, (2) water quality, especially water temperature, (3) energy inputs from the watershed, (4) biotic interaction between species, and (5) characteristics of the streamflow regime. Physical habitat is a necessary, but not sufficient, condition for aquatic animals. We present two analytic approaches potentially useful in quantifying limiting factors: quantile regression analysis and dynamic models. Both of these tools are helpful in understanding limits on the aquatic ecosystem caused by characteristics of the physical habitat and other factors. The quantile regression analysis case study presented is for twenty Atlantic salmon streams in Newfoundland Canada, and shows that physical habitat may be limiting along with organic anions and nitrates. The population model example shows that multiple factors may limit fish production, and the importance of a factor can vary between years. The population model calculates mortality as a function of the time series of water temperatures (in turn affecting in vivo egg, fry, and parr lifestages) and dynamic streamflow (affecting the probability of redd superimposition, as well as egg incubation and fry habitat quantity). In any given year, it is the combination of one or more of these factors that tend to control salmon production in the modeled river. Both techniques can help elucidate the relative contribution of the array of potential limiting factors.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".