Assessment of natural regeneration of longleaf pine (<i>Pinus palustris</i>) 15 years post-regeneration control
Bibliographic record
Abstract
We assessed natural regeneration of longleaf pine (Pinus palustris Mill.) using the data collected from the Escambia Experimental Forest in southern Alabama. Fifteen years after the regeneration control, natural regeneration of longleaf pine remained patchy across a wide range of site and stand conditions; slightly more than half of all plots contained regeneration, but the density of seedlings and saplings varied significantly. The abundance of seedlings ≤1-year-old was positively related to stand age and time since last fire, but negatively related to overstory basal area. The abundance of seedlings and saplings was positively related to stand age, but negatively related to time since last fire and overstory basal area. The probability of achieving at least 15 000 seedlings·ha–1 that are older than 1 year but less than 1 m tall and at least 1250 saplings·ha–1 that are over 1 m tall was, respectively, positively related to the ratio of time since last fire to overstory basal area and the ratio of quadratic mean diameter to site index. A longer fire interval (> 2 to 3 years) should be adopted to naturally regenerate longleaf. We did not find clear zones of exclusion present in natural regeneration even though overstory trees, seedlings, and saplings tended to be repulsive spatially and >80% grass stage seedlings and saplings occurred outside tree crowns.
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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) | 0.001 | 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".