Influence of fertilization, weed control, and pine litter on loblolly pine growth and productivity and understory plant development through 12 growing seasons
Bibliographic record
Abstract
On a silt loam soil in central Louisiana, three cultural treatments were applied to a seedling loblolly pine (Pinus taeda L.) plantation. The treatments were in a 2 × 2 × 2 factorial design: (1) no fertilization or a broadcast application of 177 kg N/ha and 151 kg P/ha; (2) no herbicides applied or broadcast or spot applications of hexazinone, sulfometuron methyl, or glyphosate herbicides and felling as required to control competing vegetation during the first three growing seasons; and (3) no litter applied or broadcast application of pine litter to form a 10 to 15 cm layer in the first growing season. Through 12 growing seasons, the fertilization or herbicide treatment significantly increased stand growth (α = 0.05), and these two treatments had an additive effect (no treatments, 209 m3/ha; fertilization, 328 m3/ha; herbicide, 280 m3/ha; fertilization and herbicide, 362 m3/ha). However, because litter application probably had a minor fertilization effect, the fertilizer and litter combination produced the greatest yield (370 m3/ha). The herbicide and litter combination adversely affected pine survival, and so applying all three treatments was no more effective than fertilization alone. The loblolly pine overstory was the dominant factor influencing the long-term development of the understory.
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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) | 0.000 | 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".