Response of small mammals to alternative stand structures in the mixed-conifer forest of northeastern CaliforniaThis article is one of a selection of papers from the Special Forum on Ecological Studies in Interior Ponderosa Pine — First Findings from Blacks Mountain Interdisciplinary Research.
Bibliographic record
Abstract
A common, but largely untested, strategy for maintaining forest biodiversity is to enhance stand structural complexity. A silvicultural experiment was implemented from 1996 to 1998 at Blacks Mountain Experimental Forest, California, to test the efficacy of two levels of structural diversity (high versus low) and two levels of prescribed underburning (burn versus no burn) for maintaining or restoring biodiversity. Small mammals were trapped and tagged in experimental units for 2 noncontiguous weeks in fall 2003 and 2004. Total number of captures and number of captured individuals varied by year (P < 0.002). No treatment effects were detected for all species lumped together or for the three most frequent species analyzed separately ( Tamias amoenus J.A. Allen, 1890, Peromyscus maniculatus (Wagner, 1845), and Spermophilus lateralis (Say, 1823)), with the exception that T. amoenus was captured more often in burned units in 2004 (P = 0.004 for year × burn interaction). Mixed-effects regression models indicated that the number of captures and captured individuals of T. amoenus and P. maniculatus decreased with increasing residual basal area of overstory trees, but opposite results were obtained for S. lateralis. After accounting for residual stand density differences, T. amoenus was captured more frequently in units of low structural diversity and S. lateralis in units of high structural diversity.
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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 | 0.001 |
| 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) | 0.001 | 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".