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Record W2039804302 · doi:10.1139/x02-039

Effect of nursery conditioning treatments and fall fertilization on survival and early growth of <i>Pinus taeda</i> seedlings in Alabama, U.S.A.

2002· article· en· W2039804302 on OpenAlexvenueno aff
David B. South, D. G.M. Donald

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationLoblolly pineSeedlingLoamHectarePinus <genus>FertilizerHorticultureConditioningAnimal sciencePruningBiologyBotanyAgronomySoil waterMathematicsEcology

Abstract

fetched live from OpenAlex

Four seedling conditioning treatments and four fall fertilization treatments were applied to loblolly pine (Pinus taeda L.) seedlings in a bare-root nursery in Alabama. Three conditioning treatments involved undercutting plus root wrenching (two, four, and six times), and a fourth treatment included top-pruning (three times) and no undercutting. Fertilizer treatments consisted of (i) control, (ii) 150 kg/ha of N, (iii) 150 kg/ha of N plus 150 kg/ha of P, and (iv) 150 kg/ha of K. On two sites, seedlings were planted in November, and an equal number were placed in cool storage and planted 6 weeks later in mid-December. Results 5 years after outplanting were generally similar for both sites; however, experimental error terms were higher on the sandy site. As a result, conditioning and fertilizer treatments had a statistically significant effect on volume per hectare at the loamy site but not at the sandy site. Volume per hectare was enhanced by undercutting in August followed by two root wrenchings and fall fertilization with N plus P. Storing seedlings reduced height, groundline diameter, and volume per hectare at both sites and reduced survival at the loamy site. Survival of both freshly planted and stored seedlings was greater than 71% at both sites.

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)0.000
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 score0.011
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.272
Teacher spread0.246 · 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

Citations22
Published2002
Admission routes1
Has abstractyes

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