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Record W2301586945 · doi:10.1890/15-1597.1

Carbon‐equivalent metrics for albedo changes in land management contexts: relevance of the time dimension

2016· article· en· W2301586945 on OpenAlexaff
Ryan M. Bright, Wiley Bogren, Pierre Y. Bernier, Rasmus Astrup

Bibliographic record

VenueEcological Applications · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNatural Resources Canada
FundersU.S. Forest ServiceNorges Forskningsråd
KeywordsAlbedo (alchemy)Environmental scienceClimate changeAtmospheric sciencesDimension (graph theory)Land managementRelevance (law)Greenhouse gasEnvironmental resource managementClimatologyLand usePhysical geographyGeographyEcologyMathematicsBiologyGeologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Surface albedo is an important physical property by which the land surface regulates climate. A wide and growing body of literature suggests that failing to account for surface albedo can result in suboptimal or even counterproductive climate‐motivated policies of the land‐based sectors. As such, albedo changes are increasingly included in climate impact assessments of forestry and other land sector projects through conversion of radiative forcings into carbon or carbon dioxide equivalents. However, the prevailing methodology does not sufficiently accommodate dynamic albedo changes on land or CO 2 in the atmosphere. We present two new metrics designed to address these deficiencies, referring to them as the time‐dependent emissions equivalent and the time‐independent emissions equivalent of albedo changes. We demonstrate their application in various land management contexts and discuss their merits and uncertainties.

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 score0.183
Threshold uncertainty score0.294

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)0.0000.000

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 head0.008
GPT teacher head0.210
Teacher spread0.203 · 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
Published2016
Admission routes1
Has abstractyes

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