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Record W2059115265 · doi:10.1139/x00-066

Using foliar variables to predict the response of lodgepole pine to nitrogen and sulphur fertilization

2000· article· en· W2059115265 on OpenAlexvenueno aff
R. P. Brockley

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationPinus contortaBasal areaFertilizerNitrogenPinus <genus>NutrientNitrogen fertilizerBotanyAnimal scienceBiologyHorticultureAgronomyChemistryEcology

Abstract

fetched live from OpenAlex

Six-year basal area responses to nitrogen (N) fertilizer, alone and in combination with sulphur (S), in 31 lodgepole pine (Pinus contorta Dougl. var. latifolia Engelm.) stands in the interior of British Columbia were compared with pre-treatment measures of foliar N and S and first-year increases in fascicle mass to determine the utility of these variables for predicting growth response and for determining appropriate fertilizer prescriptions. Results indicate that pre-fertilization levels of foliar N or inorganic sulphate-S (SO 4 ) may be more reliable than the fascicle mass screening method for predicting whether or not a significant stemwood response will occur following N fertilization. When combined, N and SO 4 levels in unfertilized foliage explained 68% of the variation in relative basal area response to fertilization with N alone. Foliar nutrients were also useful for determining whether or not lodgepole pine would respond incrementally to S when added in combination with N. Stands in which pre-fertilization foliar SO 4 was [Formula: see text]60 mg/kg and N/S ratio was [Formula: see text]13 did not respond significantly to N alone but always responded significantly to N + S. Conversely, a foliar SO 4 level &gt;60 mg/kg combined with a N/S ratio of [Formula: see text]12 always resulted in a favourable response to N with no incremental benefit of added S.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.296
Teacher spread0.261 · 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.

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

Citations47
Published2000
Admission routes1
Has abstractyes

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