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Record W2077300397 · doi:10.1139/x10-136

Effects of two Ca fertilizer types on sugar maple vitality

2010· article· en· W2077300397 on OpenAlexaffvenue
Jean‐David Moore, Rock Ouimet

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsMinistère des Ressources naturelles et des ForêtsMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsMapleSugarLimeAceraceaeBotanyVitalityHorticultureChemistryBiologyFood science

Abstract

fetched live from OpenAlex

Over the last decades, continuous signs of sugar maple ( Acer saccharum Marsh.) dieback in stands of northeastern North America have promoted the experimentation of corrective measures to restore sugar maple vitality. To verify the hypothesis that K–Mg antagonism may have limited the full response of sugar maple to dolomitic lime application in a previous experiment (CaMg(CO 3 ) 2 , 12% Mg), two Ca fertilizers (CaCO 3 and CaSO 4 ·2H 2 O), having negligible Mg content, were applied at rates of 1, 2, and 4 t Ca·ha –1 on sugar maple trees adjacent to the limed area. After 3 years, most of the foliar nutrient concentrations of treated trees were improved, particularly Ca, for both Ca fertilizers, in line with published ranges for healthy sugar maple trees, except for Mg. Moreover, no persistent nutrient antagonism was observed. The crown dieback rate of treated sugar maple was ≤5.8% after 3 years, while it reached 12% for the controls. Also, relative basal area growth showed that both Ca sources can improve growth rate. Growth response following Ca treatments was, however, lower than for the former lime experiment after the same period of time. In this context, our results suggest that Mg nutrition could be more important for sugar maple in this ecosystem than initially thought.

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 score0.363
Threshold uncertainty score0.954

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.307
Teacher spread0.280 · 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

Citations25
Published2010
Admission routes2
Has abstractyes

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