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Record W2810072327 · doi:10.1007/s13595-018-0743-5

The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3): customization of the Archive Index Database for European Union countries

2018· article· en· W2810072327 on OpenAlex
Roberto Pilli, Stephen Kull, Viorel Blujdea, Giacomo Grassi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAnnals of Forest Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersJoint Research Centre
KeywordsMetadataDatabaseContext (archaeology)Transparency (behavior)European unionIndex (typography)Computer scienceEnvironmental resource managementWorld Wide WebBusinessGeographyEnvironmental scienceInternational trade

Abstract

fetched live from OpenAlex

Abstract Key message The purpose of this report is to increase the transparency of applications of the CBM-CFS3 model by climate-related policy-makers and researchers. The report provides explicit information on the parametrization of a new Archive Index Database used with this model to simulate forest carbon dynamics in 26 EU countries. The database can be accessed at https://data.europa.eu/89h/jrc-cbm-eu-aidb , primary metadata are available in Kull et al. ( 2017 ), and additional metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/df48155b-973f-4169-a722-100bb6bfc76c . The Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) has been adapted, tested, and applied to forests of 26 EU countries over the last 7 years for EU policy making and scientific research. The overall purpose of this exercise is to increase the transparency of how the EU Archive Index Database (EU-AIDB) was parameterized while supporting both the policy making and research communities interested in applying the CBM-CFS3 with ecological parameters specific to the EU context. In addition to preparing model input data reflecting various management and disturbance scenarios for CBM-CFS3 projects, an essential step was to update the original AIDB with information specific to the EU context and create an EU-AIDB. The AIDB is the Microsoft Access database behind the CBM-CFS3 that stores default ecological information and parameters pertaining to the forest ecosystems of a country, among other functions. The EU-AIDB incorporates 1034 spatial units resulting from the intersection of 204 European administrative regions and ecological boundaries representing 35 climatic units. It also contains updated parameters for 192 of the main tree species reported by the National Forest Inventories of each EU country. The release of this database allows CBM-33 CFS3 users in the EU to apply European administrative and ecological units and tree species in forest carbon modeling projects.

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.

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)0.002
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 score0.482
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.026
GPT teacher head0.260
Teacher spread0.234 · 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