CGIAR Research Program on Climate Change, Agriculture and Food Security - Plan of Work and Budget 2017
Notice bibliographique
Résumé
A. CRP LEVEL A1. Delivery A1.1 Adjustments/ changes to your Theories of Change (ToC)The ToC remains the same as was presented in the proposal.Efforts in FP2 will be made to set up robust monitoring methods in all Climate-Smart Villages (CSVs) to ensure evidence creation and learning.FP3 will place greater emphasis on supporting the processes of the Nationally Determined Contributions (NDCs) to the 2015 UNFCCC Paris Agreement, as these have become important as policy drivers.Minor adjustments are planned in FP4 to the types of next users, as donor climate screening processes have emerged as a promising area driving investments. A1.2 Highlight expected Outcomes and OutputsIn FP1, multi-level scenario methods will be developed in all target regions, building on Phase I work but now with a more explicit focus on food and nutrition security, and gender and social inclusion.FP1 projects will contribute evidence to the targeting, priority setting, policy and investment decision making in all regions.Current CCAFS experience to date with science-policy linkages will be synthesised, partly to begin testing the ToC.A set of comparative analyses of enabling policy environments (especially food and nutrition security policies) with respect to gender equity considerations will be undertaken.In FP2, emerging results from action-research in CSVs will continue to be produced, and fed into development programs, generating wide-scale promotion and adoption of CSA.Strategic support will be provided to major development and private sector investments in CSA, including on metrics, prioritization tools, business models, gender and CSA approaches and synthesised knowledge products for national and sub-national scales.Significant effort will be made to identify and synthesise opportunities for aligning agricultural and climate finance to CSA, and will culminate in a high level event to bring major finance and agriculture actors together.Sex-disaggregated data in relation to CSA will be collected and synthesised in CSVs.In FP3, major outcomes include the Government of Kenya's use of data on LED dairy intensification value chains for a proposal to the Green Climate Fund (GCF) and the use by 15-20 countries of a global review of MRV methods for livestock emissions (with the GRA, FAO, WB).New outputs will include national assessments of mitigation options in four countries, data on finance needs to inform national investment plans in three countries, summary of synergies between adaptation and mitigation, and a scoping of priorities for reducing emissions through decreased food loss and waste, including analysis of women and youth dimensions.In FP4, expanded tools for meteorological data quality control and reconstruction, and a suite of agriculture-focused "Maproom" products, will enhance capacity of national and regional (ICPAC, AGRHYMET) climate information providers.Univ. of Reading's PICSA approach for communicating climate information with rural communities will be expanded, and scaled up (Rwanda, Ghana) and out (Senegal, Colombia, Central America).Work on evidence and guidance will support women's access to resources through gender-sensitive climate services, advisories and safety nets. A.1.3 Use of different Funding SourcesW1-2 funds are used to fund the core elements of the CCAFS strategy, as described in this POWB.W3bilateral funds are only accepted if aligned with the strategy; and usually contribute to specific cases studies in particular countries (see Flagships for further details). A1.4 Planned Revisions to your Program of WorkNo substantial changes are planned for FP1, FP2 and FP3.Loss of a project on agricultural disease early warning systems due to a budget cut to FP4 will reduce attention to government and humanitarian users of climate-related early warning systems.FP4 consolidated two strongly overlapping 2017 milestones, and adjusted 2017 milestone wording to align better with activities and deliverables.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,006 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,182 | 0,094 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».