Assessment of reproductive requirements in habitat conservation efforts: a case study on blackside dace (<i>Chrosomus cumberlandensis</i>), a federally listed threatened species
Bibliographic record
Abstract
Species conservation efforts often fail to consider specific-species reproductive requirements. This omission is understandable given that collection of such data must focus on individual taxa and is potentially disruptive. However, spawning requirements remain a vital component in long-term conservation efforts. Using blackside dace (Chrosomus cumberlandensis), a federally listed threatened species, we demonstrate how reproductive habitat can be unobtrusively quantified to improve restoration efforts. Reproductive habitat characteristics are unquantified for blackside dace. However, these fish spawn in certain areas, suggesting they have specific reproductive microhabitat requirements. The physical habitat of nests and surrounding areas was characterized by assessing depth, stream flow, and physical structure, which were correlated with nest activity. Flow and depth were the most influential factors in determining nest location and nest activity, respectively, and nest activity was positively correlated with substrate size. These data are a critical component for ongoing habitat restoration and captive breeding efforts for this species, providing the foundation for recreating essential reproductive habitat. Our study emphasizes the importance of understanding a species’ reproductive habitat requirements and ecology to aid habitat restoration efforts for threatened species.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".