MétaCan
Menu
Back to cohort
Record W1992271245 · doi:10.1139/x06-307

Soil and foliar chemistry associated with potassium deficiency in Pinus radiata

2007· article· en· W1992271245 on OpenAlexvenueno aff
P. J. Smethurst, Andrew Knowles, Keith Churchill, Ann Wilkinson, Arthur Lyons

Bibliographic record

VenueCanadian Journal of Forest Research · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsPinus radiataHuman fertilizationRadiataSeedlingAgronomyFertilizerSoil waterChemistryWeed controlBotanyBiologyVignaEcology

Abstract

fetched live from OpenAlex

We required an improved understanding of K deficiency and diagnosis in Pinus radiata D. Don (radiata pine). A rapid growth response to K fertilization (100 kg·ha –1 ) in the presence of weed control confirmed K deficiency in a 2-year-old stand. Tree growth did not respond to N fertilization, and weed control alone appeared insufficient to maximize tree growth. Temporal patterns in visual symptoms of K deficiency suggested they were worst at the end of a drought, and improved after several months of above-average rainfall that coincided with an increase in soil temperature. Soil chemistry generally responded predictably to fertilization, but K fertilization increased soil solution Ca and Mg concentrations without changing exchangeable concentrations. With weed control, a doubling of stem growth response to K fertilizer was associated with a 270% increase in soil solution K (natural variation amongst control plots), 51% increase in exchangeable K, and 39% increase in foliar K. Relationships between seedling growth and hydroponic concentrations of K were consistent with the incidence of K deficiency in the field; predicting these deficiencies using soil exchangeable concentrations was less clear. This study advances the interpretation of soil and foliar chemistry in relation to K and Mg deficiency in P. radiata.

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)0.003
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 score0.603
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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

Citations17
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicSeedling growth and survival studiesFrench-language works237,207