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Record W4398222540 · doi:10.1038/s43247-024-01437-0

The qualified prevalence of natural and planted tropical reforestation

2024· article· en· W4398222540 on OpenAlexafffund
Sean Sloan

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsVancouver Island University
FundersCanada Research Chairs
KeywordsReforestationAgroforestryNatural (archaeology)TropicsGeographyForestryEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Recent satellite estimates suggest that planted tree cover rivals, and possibly exceeds, the area of natural reforestation pantropically, challenging longstanding models of forest change. Such estimates underscore a tension between studies of reforestation as an areal expansion of undifferentiated forest cover versus dynamic land-change processes by which forest variously emerges in transformed states. A review of land-change processes bearing on the nature of reforestation would qualify the relative prevalence of planted tree cover, but with caveats. Planted tree cover would be less than half the nominal extent of natural reforestation if including the 29-61% of natural reforestation re-cleared within 15 years and excluding the 25-50% of planted tree-cover entailing extant forest conversion. Planted tree cover would however be comparable to natural reforestation if also discounting the 31-52% of natural reforestation that similarly follows from forest conversion. Satellite-based estimations of reforestation area may now, and should, incorporate such qualifying land-change processes by borrowing from demographic models of population change and including ‘spurious’ reforestation integral to the broader processes of reforestation of interest.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.272

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 science0.0000.001
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 head0.017
GPT teacher head0.226
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.

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

Citations6
Published2024
Admission routes2
Has abstractyes

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