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Record W2342321623 · doi:10.1007/s00442-016-3621-6

Viewing forests from below: fine root mass declines relative to leaf area in aging lodgepole pine stands

2016· article· en· W2342321623 on OpenAlexafffund
A. S. Schoonmaker, Victor J. Lieffers, Simon M. Landhäusser

Bibliographic record

VenueOecologia · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of AlbertaNorthern Alberta Institute of Technology
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Alberta
KeywordsChronosequencePinus contortaBiologyProductivitySpecific leaf areaShadingLeaf area indexSite indexAgronomyBotanyForestryHorticultureEcologyEcosystemPhotosynthesisGeography

Abstract

fetched live from OpenAlex

In the continued quest to explain the decline in productivity and vigor with aging forest stands, the most poorly studied area relates to root system change in time. This paper measures the wood production, root and leaf area (and mass) in a chronosequence of fire-origin lodgepole pine (Pinus contorta Loudon) stands consisting of four age classes (12, 21, 53, and ≥100 years), each replicated ~ five times. Wood productivity was greatest in the 53-year-old stands and then declined in the ≥100-year-old stands. Growth efficiency, the quantity of wood produced per unit leaf mass, steadily declined with age. Leaf mass and fine root mass plateaued between the 53- and ≥100-year-old stands, but leaf area index actually increased in the older stands. An increase in the leaf area index:fine root area ratio supports the idea that older stand are potentially limited by soil resources. Other factors contributing to slower growth in older stands might be lower soil temperatures and increased self-shading due to the clumped nature of crowns. Collectively, the proportionally greater reduction in fine roots in older stands might be the variable that predisposes these forests to be at a potentially greater risk of stress-induced mortality.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.231
Teacher spread0.218 · 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 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

Citations19
Published2016
Admission routes2
Has abstractyes

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