Investigation into the role of annexin 1 in the microcirculation of annexin 1 knockout mice
Bibliographic record
Abstract
Food insecurity is an important global problem severely affecting developing countries, particularly those in Asia and Africa.Agricultural research in developing countries is characterised by the following tension: the private sector has plenty of applied research skills and experience but these are primarily used for commercial gain; the public sector has excellent research but the research is often not applied.Agricultural public private partnerships are currently acclaimed as a means of redressing this tension through optimising the complementary synergies between the two sectors in order to address food security.Private sector involvement in agriculture, including public private partnerships (PPPs) has increased in the past two decades as has the use of intellectual property rights (IPRs) in agriculture research.The two sectors have differing and sometimes conflicting perspectives on IP as a concept and in the strategies used to manage intellectual property.IPRs have the potential to enhance or hinder the achievement of a partnership's objectives.This thesis investigates whether, to what extent and in what ways IP is relevant to food security oriented PPPs.It uses two case studies in India and Kenya involving two centres in the Consultative Group on International Agriculture Research (CGIAR) to locate the role that IP plays in the formation and execution of food security oriented PPPs in the context of development.It argues for a bespoke analysis of PPPs as the preferred means through which the impact and effect of factors such as IPRs can be meaningfully examined.It finds that the relevance of IP to food security oriented PPPs in developing countries is determined by two factors: the nature of the technology used in the partnership and the stage of the partnership.This research would not have been possible without the generous funding from the Kirkhouse Trust.Many thanks to Sonia
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".