Fire history of pinyon–juniper woodlands at upper ecotones with ponderosa pine forests in Arizona and New Mexico
Bibliographic record
Abstract
We used maps of fire evidence, fire scar dendrochronology, forest age-structure analysis, and landscape analysis to investigate fire history at pinyon pine ( Pinus edulis Engelm.) – juniper ( Juniperus osteosperma (Torr.) Little, Juniperus scopulorum Sarg.) woodland – ponderosa pine ( Pinus ponderosa P. & C. Lawson) forest ecotones in Arizona (Tusayan) and in New Mexico (Canjilon). Results showed that charred trees were not evenly distributed across vegetative communities but were significantly (p < 0.001) more abundant than expected in ponderosa pine communities. Composite fire scar analysis indicated that surface fires occurred in ponderosa pine stands at both sites and burned at intervals of 7.2–11.1 years (WMPI; Weibull median probability interval). At Tusayan, landscape structure was fine grained, and maximum pinyon age was >200 years across 80% of the site. At Canjilon, landscape pattern was relatively coarse, and most pinyon patches were 200–300 years old. Cumulative standing age distributions suggested pinyon–juniper fire rotations of 340 and 290 years at Tusayan and Canjilon, respectively. We concluded the following: (i) surface fires in ponderosa pine stands did not spread through pinyon–juniper communities at either site, (ii) fire evidence was prevalent across both sites, but old pinyon trees indicated that no widespread lethal fires had occurred in the last 300–400 years, and (iii) structurally heterogeneous landscapes suggested that historical pinyon–juniper fires were of limited extent but lethal in patches.
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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 | 0.001 |
| 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".