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Record W2789450239 · doi:10.1093/treephys/tpy010

Age-related changes in survival and turnover rates of balsam fir (Abies balsamea (L.) Mill.) fine roots

2018· article· en· W2789450239 on OpenAlexafffund
M. J. Krasowski, M. B. Lavigne, Michael A Szuter, Jakub Olesiński, John A. Kershaw, Elizabeth McGarrigle

Bibliographic record

VenueTree Physiology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsNova Scotia Department of AgricultureNatural Resources CanadaCanadian Forest ServiceUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbies balsameaBalsamAbies albaDemographyBiologyEcologyBotanyPicea abies

Abstract

fetched live from OpenAlex

Fine-root (≤2 mm) demographics change as forests age, but the direction and extent of change are unknown. Knowledge of the change and understanding of causes will improve predictions of climate change impacts. We used minirhizotrons at three young and three mature balsam fir (Abies balsamea (L.) Mill.) sites to measure median lifespan (MLS) for each site and for annual cohorts. We computed turnover rate from the inverse of MLS (Tinv) and calculated a second turnover rate (T) from annual mortality, annual production and previous year-end standing crop. Median lifespan at mature sites (436 days) was half that at young sites (872 days). Median lifespan of annual cohorts varied widely at all sites. Age-class distributions of fine roots seen by minirhizotrons changed with increasing years of observation, with older age classes accumulating more slowly at mature sites. Our findings highlight the need to determine whether the proportional contributions of absorbing and transporting fine roots to annual production and their median lifespans change during stand development. Due to its variation among annual cohorts, we believe robust estimates of MLS at our sites require 5-7 years of observation, and reliable estimates of Tinv are reached earlier than T.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.241
Teacher spread0.229 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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