Influence of fire on native nitrogen-fixing plants and soil nitrogen status in ponderosa pine - Douglas-fir forests in western Montana
Bibliographic record
Abstract
Nitrogen fixing plants have been reported to play an important role in replacing N lost from soil in fire dominated ecosystems. Exclusion of fire from ponderosa pine (Pinus ponderosa Dougl. ex Laws.) - Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests of western Montana has lead to widespread changes in forest structure, composition, and function including a potential reduction in the occurrence of N-fixing plant species. We investigated the effect of fire exclusion and reintroduction of fire on the frequency, occurrence, and function of native N-fixing plant species at 11 paired burned and unburned sites in western Montana. These pairs had been either undisturbed since the early 1900s or had been repeatedly opened by logging and (or) fire over the last 80-100 years. Although the percent cover of N-fixing plants was low at all sites, the cover and frequency of N-fixing plants were significantly greater in sites exposed to fire than in the unburned sites and greater in repeatedly opened sites than in undisturbed sites. In contrast, levels of available N were significantly lower in burned sites compared with unburned sites and in repeatedly opened sites. Nitrogen-fixing plants may have played an important role in maintaining productivity in frequently burned ponderosa pine forests but now appear to be suppressed in fire-excluded forests.
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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 | 0.001 | 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".