Bibliographic record
Abstract
Two subspecies of Black Rail Laterallus jamaicenis occur in the United States, and neither has been studied extensively. Of the two, the Eastern subspecies L. j. jamaicenis has a larger range, but has been studied to a lesser degree than the California subspecies (L. j. coturniculus Eastern Black Rail are known to breed at several locations in Kansas, but as in other inland populations, precisely where these individuals overwinter is unknown. Additionally, little information is available on characteristics of breeding habitat for inland Eastern Black Rail populations, and few studies have investigated the effect of habitat management techniques on these populations. Eastern Black Rail are rnost often observed in Kansas at Quivira National Wildlife Refuge NWR and private lands surrounding it. Call playback surveys were conducted in the summers of 2009 and 2010 to locate breeding individuals and identify nesting habitat. Drift-fences and traps were set in locations where individual Black Rail were detected, and sound samples were used to attract individuals for capture. Rectrices and body coverts were plucked from similar regions on two captured individuals and used for deuterium stable isotope analysis. Coverts had average deuterium values of -86.2 and -77.8 per mil, respectively. These values are more typical for southern Canada and portions of the western United States. These feather values suggest that Kansas occurring Black Rail either winter away from the Gulf Coast, in contrast to current understanding, or grow feathers during or soon after spring migration. Quivira NWR and surrounding private lands use prescribed burning, grazing and haying to manage vegetation in the wet-meadow habitat that the rails typically occupy. To characterize breeding habitat, I quantified vertical vegetation structure, water presence and depth, and plant height in areas where Black Rail responded during playback surveys. A Kruskal-Walis one-way analysis of variance was used to compare these variables among 13 treatment types. Although test results indicated these variables were significantly different among treatment types, a nonparametric Tukey's post-hoc test could not detect where the differences occurred. A backward stepwise (Wald) logistic regression indicated higher percentages of dead vegetation in upper vertical layers and plant height positively influenced rail presence, whereas a higher percentage of living vegetation at lower layers negatively influenced rail presence (Nagelkerke R Square 0.57, p < 0.001). Black Rail were most often detected in sections witl1 moderate levels of disturbance (e.g., burned annually, burned and grazed), while areas with higher levels of disturbance ( e.g., annual haying, haying and burning) did not appear to possess suitable habitat, as no rails were detected in these locations. J:vloderately disturbed areas, such as those burned every two years, might contain the mosaic of living and dead vegetation necessary for Black Rail nesting habitat in this portion their breeding range.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".