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Record W2141667279 · doi:10.1139/x07-191

Growth and physiology of loblolly pine in response to long-term resource management: defining growth potential in the southern United States

2008· article· en· W2141667279 on OpenAlexvenueno aff
Lisa J. Samuelson, John R. Butnor, Chris Maier, Tom A. Stokes, Kurt H. Johnsen, Michael B. Kane

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsFertigationLoblolly pineBiomass (ecology)IrrigationAgronomyWeed controlBiologyProductivityWeedBasal areaBotanyPinus <genus>Ecology

Abstract

fetched live from OpenAlex

Leaf physiology and stem growth were assessed in loblolly pine ( Pinus taeda L.) in response to 10 to 11 years of treatment with weed control (W), weed control plus irrigation (WI), weed control plus irrigation and fertigation (WIF), or weed control plus irrigation, fertigation, and pest control (WIFP) to determine whether increased resource availability can push productivity of loblolly pine closer to its biological growth potential expressed in favorable, exotic environments. Maximum basal area and stem biomass were 41 m 2 ·ha –1 and 172 Mg·ha –1 , respectively, in response to fertigation. Stemwood biomass production was positively and linearly related to basal area. Belowground woody biomass was highest in the WIF and WIFP treatments and averaged 50 Mg·ha –1 , but the W and WI treatments exploited a greater area of soil with low-density coarse roots. Fertigation increased foliar nitrogen concentration and foliage biomass, but treatment had no effect on leaf physiological parameters or growth efficiency. Comparison with growth rates reported for loblolly pine in Hawaii revealed that loblolly pine grown in its native range can produce the high yields observed in exotic environments when stands are below maximum carrying capacity.

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)0.002
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 score0.160
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.250
Teacher spread0.234 · 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

Citations81
Published2008
Admission routes1
Has abstractyes

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