CGIAR Research Program on Climate Change, Agriculture and Food Security - Plan of Work and Budget 2017
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.182 | 0.094 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".