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Record W2163875956 · doi:10.1139/x99-202

Impact of precommercial thinning in balsam fir stands on soil nitrogen dynamics, microbial biomass, decomposition, and foliar nutrition

2000· article· en· W2163875956 on OpenAlexvenueaboutno aff
Lucie Thibodeau, Patricia Raymond, Claude Camiré, Alison D. Munson

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBalsamAbies balsameaThinningEnvironmental scienceAgronomyBiomass (ecology)Mineralization (soil science)BotanyBiologyEcologySoil waterSoil science

Abstract

fetched live from OpenAlex

Precommercial thinning is being tested in Quebec as a preventive silvicultural treatment to reduce vulnerability of young balsam fir (Abies balsamea (L.) Mill.) stands to spruce budworm (Choristoneura fumiferana (Clem.)) damage and to shorten rotations. As part of a larger study of ecosystem response to thinning, we have examined the impact of this treatment on soil nitrogen dynamics, microbial biomass, cellulose decomposition, and foliar nutrition across a range of drainage conditions (good, imperfect, and poor). In the first year after thinning, initial early season ammonium (NH 4 + -N) pools in the mineral horizon were significantly higher in the thinned plots (P = 0.019), while net nitrogen mineralization (NH 4 + -N plus NO 3 - -N) decreased in these same plots (P = 0.052). The thinning treatment significantly increased microbial biomass nitrogen (N mic ) in the organic horizon (P = 0.051). Simple regression analysis indicated the importance of soil temperature in controlling N mic . Decomposition of cellulose substrate in the organic horizon was significantly increased by thinning, and mass loss was related to soil temperature. Increased decomposition and nutrient availability after thinning were reflected in improved N, P, and K nutrition in current, 1- and 2-year-old balsam fir needles. The temporal extent of this improved fertility will be verified by longer term monitoring.

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)0.000
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 score0.442
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.300
Teacher spread0.286 · 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

Citations164
Published2000
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207