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Record W2734750534 · doi:10.1088/1748-9326/aa7394

Impact of deforestation on subsurface temperature profiles: implications for the borehole paleoclimate record

2017· article· en· W2734750534 on OpenAlexafffund
Andrew H. MacDougall, Hugo Beltrami

Bibliographic record

VenueEnvironmental Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à MontréalSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsDeforestation (computer science)BoreholePaleoclimatologyEnvironmental scienceClimatologyClimate changeAnomaly (physics)Climate modelGlobal warmingGeologyGlobal coolingAtmospheric sciences

Abstract

fetched live from OpenAlex

Subsurface temperature profiles measured in boreholes are one of the important archives of paleoclimate data for reconstructing the climate of the past 2000 years. Subsurface temperatures are a function of past ground surface temperatures (GST), however GSTs are influenced both by changes in land-use and changes in regional climate. Thus the history of deforestation at borehole sampling locations represents a potential uncertainty in the reconstructed temperature history at the site. Here a fully coupled Earth system model is used estimate the magnitude of the subsurface temperature anomaly from deforestation events from a global perspective. The model simulations suggest that warming of the ground surface is the dominant response to deforestation, consistent with the limited field data that exist. The magnitude of the temperature anomaly varies by environment with a global average anomaly of 0.85 °C with a range of −0.48 °C to 1.78 °C. The warming originates from a reduction in the efficiency of turbulent energy flux to the atmosphere overcompensating an increase in albedo. Overall our simulations suggest that deforestation has a large impact on subsurface temperatures for centuries following deforestation and thus GST reconstructions should take into account previous deforestation events.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.347
Teacher spread0.295 · 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

Citations26
Published2017
Admission routes2
Has abstractyes

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