Sharp-tailed Grouse (<i>Tympanuchus phasianellus</i>) population dynamics and restoration of fire-dependent northern mixed-grass prairie
Bibliographic record
Abstract
Case studies of Sharp-tailed Grouse (Tympanuchus phasianellus) population dynamics before and during re-introduction of fire to northern mixed-grass prairies that lacked fire for multiple decades are unavailable. At a 108-km2 northern mixed-grass prairie refuge in North Dakota, fire was suppressed from the early 1900s through late 1970s. Nine management units (total area 16.8 km2, 15.7% of the refuge) received initial prescribed fire treatments during 1979–1984. The mean annual density of male Sharp-tailed Grouse attending leks on these units during 1981–1985 (9.0 males/km2) was twice that on the same units during 1961–1965 (4.2 males/km2), amid the fire exclusion era; nonoverlap of 90% CIs encompassing the means suggested a significant treatment effect. However, densities of males on units managed without prescribed fire during 1961–1965 and 1981–1985 did not change between the two periods. By 1987, fire had been re–introduced to >50% of the refuge overall. Mean annual abundance (i.e., total numbers) of lekking males on the entire refuge did not differ between 1961–1965 and 1981–1985 but was significantly greater during 1989–1993 than during 1961–1965 and 1981–1985. Changes in density and abundance of lekking males coincided with fire-induced reductions in woody cover and increases in herbaceous cover. Our study illustrates the marked capacity of Sharp-tailed Grouse to respond to reductions of tree and shrub cover resulting from prescribed fire in northern mixed-grass prairie and the species’ attraction to habitat disturbance in general.
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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 | 0.000 | 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) | 0.000 | 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".