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Record W2003434367 · doi:10.1139/x03-119

Influence of fertilization, weed control, and pine litter on loblolly pine growth and productivity and understory plant development through 12 growing seasons

2003· article· en· W2003434367 on OpenAlexvenueno aff
James D. Haywood, J.C.G. Goelz, Mary Anne Sword Sayer, Allan E. Tiarks

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationAgronomyUnderstoryLoamLitterGrowing seasonGlyphosateBiologyWeed controlFertilizerEnvironmental scienceBotanyCanopySoil waterEcology

Abstract

fetched live from OpenAlex

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 m 3 /ha; fertilization, 328 m 3 /ha; herbicide, 280 m 3 /ha; fertilization and herbicide, 362 m 3 /ha). However, because litter application probably had a minor fertilization effect, the fertilizer and litter combination produced the greatest yield (370 m 3 /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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.255
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2003
Admission routes1
Has abstractyes

Explore more

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