Competition intensity varies with hardwood species identity and constrains stand-level productivity in southeastern pine–hardwood mixtures compared to loblolly pine monocultures
Bibliographic record
Abstract
We examined the performance of sweetgum ( Liquidambar styraciflua L.), cherrybark oak ( Quercus pagoda Raf.), and loblolly pine ( Pinus taeda L.) in two-species pine–hardwood mixtures (sweetgum : loblolly pine and cherrybark oak : loblolly pine) at various planting densities (1482–1976 trees per hectare (TPH)) over 23 years in northern Louisiana, USA. Species were planted in alternating rows with hardwood establishment occurring 1 year prior to loblolly pine. Mixtures were also compared to loblolly pine monocultures at a common density (1482 TPH) to assess whether mixing improved productivity. Sweetgum exerted more competitive pressure on loblolly pine than cherrybark oak. At final measurement, sweetgum survival statistically exceeded that of loblolly pine across mixture density. Moreover, sweetgum cumulative basal area and volume growth nearly tripled that of loblolly pine in balanced high-density mixtures (1976 TPH, 50 pine : 50 hardwood). In contrast, cherrybark oak basal area and volume growth did not significantly exceed loblolly pine at any density. At a common density (1482 TPH), loblolly pine monoculture cumulative basal area and volume significantly exceeded those of mixtures with sweetgum and cherrybark oak by 19% and 24%, respectively. Collectively, these results indicate that growing loblolly pine in mixture with these two species did not produce complementary interactions and negatively affected stand-level growth.
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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 | 0.001 | 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".