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Record W2046332040 · doi:10.1139/x02-073

Modeling in-situ pine root decomposition using data from a 60-year chronosequence

2002· article· en· W2046332040 on OpenAlexvenueno aff
Kim H. Ludovici, Stanley J. Zarnoch, Daniel deB. Richter

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersU.S. Forest Service
KeywordsTaprootChronosequenceRoot systemLoblolly pineBiomass (ecology)Bark (sound)Pinus pinasterEnvironmental scienceCarbon sequestrationAgronomyNutrientBotanyBiologyEcological successionPinus <genus>EcologyCarbon dioxide

Abstract

fetched live from OpenAlex

Because the root system of a mature pine tree typically accounts for 20–30% of the total tree biomass, decomposition of large lateral roots and taproots following forest harvest and re-establishment potentially impact nutrient supply and carbon sequestration in pine systems over several decades. If the relationship between stump diameter and decomposition of taproot and lateral root material, i.e., wood and bark, can be quantified, a better understanding of rates and patterns of sequestration and nutrient release can also be developed. This study estimated decomposition rates from in-situ root systems using a chronosequence approach. Nine stands of 55- to 70-year-old loblolly pine (Pinus taeda L.) that had been clear-cut 0, 5, 10, 20, 25, 35, 45, 55, and 60 years ago were identified on well-drained Piedmont soils. Taproot and lateral root systems were excavated, measured, and weighed. Although more than 50% of the total root mass decomposed during the first 10 years after harvest, field excavations recovered portions of large lateral roots (&gt;5 cm diameter) and taproots that persisted for more than 35 and 60 years, respectively. Results indicate that decomposition of total root biomass, and its component parts, from mature, clear-cut loblolly pine stands, can be modeled with good precision as a function of groundline stump diameter and years since harvest.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.338
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations71
Published2002
Admission routes1
Has abstractyes

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