A spatial autocorrelative model for targeting stream restoration to benefit sensitive nongame fishes
Bibliographic record
Abstract
Stream restoration projects often aim to benefit aquatic biota and frequently use the reappearance of sensitive nongame fish species as a measure of restoration success. However, mitigation of human influence will only benefit a given species where static habitat characteristics are suitable for that species and where potential source populations are within the range of their dispersal capability. We used spatial autoregressive habitat models to simulate the effect of watershed-scale stream restoration on the distributions of six sediment-sensitive fish species in Wisconsin, USA, streams. These models consider the probability of occurrence of a species in a given stream segment as a function of characteristics of that segment as well as the characteristics of neighboring segments. Populations of individual species are predicted to be restorable in 0.2%–2.8% of Wisconsin streams. Streams with high restoration potential for one or more species generally have high watershed human land use but are also closely connected through the stream network to relatively undisturbed streams. These results indicate that habitat restoration for nongame stream fishes will be most effective when it builds onto existing suitable habitat because of both dispersal limitation and spatial autocorrelation of habitat characteristics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".