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Record W2019511195 · doi:10.1139/x07-134

Estimation of coarse root biomass and nutrient content for sugar maple, jack pine, and black spruce using stem diameter at breast height

2008· article· en· W2019511195 on OpenAlexaffvenue
Rock Ouimet, Claude Camiré, M. Brazeau, Jean‐David Moore

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité LavalCentre de Géomatique du QuébecMinistère des Ressources naturelles et des Forêts (Québec)
Fundersnot available
KeywordsMapleSugarBlack spruceDiameter at breast heightBotanyBiomass (ecology)AceraceaeNutrientHorticultureBiologyTaigaAgronomyEcology

Abstract

fetched live from OpenAlex

Estimates of belowground biomass and mineralomass are fundamental to understanding carbon and element cycling in forest ecosystems. At two sites, we measured coarse root (diameter ≥2 mm) biomass by diameter class and their mineralomass for sugar maple ( Acer saccharum Marsh.), black spruce ( Picea mariana (Mill.) BSP), and jack pine ( Pinus banksiana Lamb.) trees to relate them to stem diameter at breast height (DBH). All regressions describing coarse root biomass and nutrient content as a function of stem DBH were highly significant (r 2 ≥ 0.89, P < 0.001). Root mineral element (N, P, K, Ca, Mg, and S) concentrations varied with tree species and root diameter class. Sugar maple roots had higher N, P, and S concentrations than the other two tree species. Black spruce had higher root Ca concentrations. Element concentrations increased consistently with the reduction of root diameter for the three studied species. We also found that the horizontal root extent of sugar maple was related to tree DBH. In conjunction with other studies, the relationship suggests that this tree species could tolerate a 10%–20% root loss but not losses ≥28%–34%; otherwise, sugar maple health and vigour would be compromised in the short term.

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 score0.120
Threshold uncertainty score0.970

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 studies0.0000.001
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 head0.064
GPT teacher head0.277
Teacher spread0.214 · 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

Citations38
Published2008
Admission routes2
Has abstractyes

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