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Record W4319723854 · doi:10.1016/j.foreco.2023.120848

TAMM review: Continuous root forestry—Living roots sustain the belowground ecosystem and soil carbon in managed forests

2023· article· en· W4319723854 on OpenAlexaff
Cindy E. Prescott, S. J. Grayston

Bibliographic record

VenueForest Ecology and Management · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental scienceBiodiversityEcosystemSoil organic matterAgroforestrySoil carbonAgronomyEcologyBiologySoil waterSoil science

Abstract

fetched live from OpenAlex

An estimated half of the carbon fixed by trees is transported belowground, a portion of which is exuded into the soil where it fuels a complex belowground food web. The biological transformation of exudates into microbial metabolites and necromass is a major source of soil organic matter (SOM), including persistent mineral-associated organic matter (MAOM). Recent recognition of the fundamental importance of these inputs from living roots for sustaining life belowground and replenishing SOM demands a rethinking of how we harvest forests. By severing the lifeline of living roots, clearcut harvesting devastates much of the belowground biodiversity in forests, and prohibits a principal pathway through which SOM and C stocks are replenished. Retention harvesting retains the influence of living roots within retention patches and potentially throughout the harvested area, but only if inter-tree distances are 15 m or less. Retention trees sustain and support the re-establishment of belowground life and function following forest harvest and may mitigate post-harvest soil C losses. Sustaining the belowground ecosystem via inputs from living roots is an underappreciated benefit of continuous-cover and retention forestry.

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 score0.317
Threshold uncertainty score0.695

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)0.0000.000

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 head0.007
GPT teacher head0.208
Teacher spread0.200 · 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

Citations52
Published2023
Admission routes1
Has abstractyes

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