Fish-habitat modeling for gap analysis to conserve the endangered Topeka shiner (<i>Notropis topeka</i>)
Bibliographic record
Abstract
Decision support tools that predict fish distribution over broad spatial scales are needed to assist in planning watershed management and endangered species recovery. We developed a geographical information system model with multivariate logistic regression to rank valley segments for probable occurrence of the endangered Topeka shiner (Notropis topeka) using stream condition variables (stream size, groundwater potential, channel slope, streamflow, network position) and land-cover variables (percent pasture, percent trees) in streams characteristic of the North American Great Plains. The stream condition and land-cover models correctly classified 89% and 68% of outcomes (i.e., presence or absence), respectively. Field tests of maps of predicted species distribution resulted in more species occurrences than expected in valley segments classified as high potential for presence and less than expected in low-potential valley segments. Gaps between high-priority segments and protected land parcels were found in all basins. In 37 basins with Topeka shiners, protected land coverage was <1% in 17 basins, 15% in 10 basins, and 521.8% in 10 basins. Conservation activities in gaps are long-term conservation measures, but maps of predicted species distribution have many immediate applications.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".