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Record W1983043064 · doi:10.2134/jeq2002.1676

Basal Area Growth of Sugar Maple in Relation to Acid Deposition, Stand Health, and Soil Nutrients

2002· article· en· W1983043064 on OpenAlexaboutno aff
Louis Duchesne, Rock Ouimet, Daniel Houle

Bibliographic record

VenueJournal of Environmental Quality · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsMapleNutrientSoil waterAceraceaeDeposition (geology)Basal areaSugarTree healthLeaching (pedology)Soil acidificationEnvironmental chemistryChemistrySoil pHEnvironmental scienceAgronomyAcid depositionEcosystemForest ecologyWeatheringBotanyEcologySoil scienceBiology

Abstract

fetched live from OpenAlex

Previous studies have shown in noncalcareous soils that acid deposition may have increased soil leaching of basic cations above the input rate from soil weathering and atmospheric depositions. This phenomenon may have increased soil acidity levels, and, as a consequence, may have reduced the availability of these essential nutrients for forest growth. Fourteen plots of the Forest Ecosystem Research and Monitoring Network in Québec were used to examine the relation between post-industrial growth trends of sugar maple (Acer saccharum Marsh.) and acid deposition (N and S), stand decline rate, and soil exchangeable nutrient concentrations. Atmospheric N and S deposition and soil exchangeable acidity were positively associated with stand decline rate, and negatively with the average tree basal area increment trend. The growth rate reduction reached on average 17% in declining stands compared with healthy ones. The results showed a significant sugar maple growth rate reduction since 1960 on acid soils. The appearance of the forest decline phenomenon in Québec can be attributed, at least partially, to soil acidification and acid deposition levels.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.585
Threshold uncertainty score0.834

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.239
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations183
Published2002
Admission routes1
Has abstractyes

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