Feasibility Assessment of Climate Adaptation in Data Sparse Contexts
Bibliographic record
Abstract
With mounting climate impacts, information to guide the deployment of climate adaptation technologies is urgently needed.However, in areas of high need, these assessments are hindered by an absence of data.Here, I look at data availability, its implications for assessment, and opportunities to draw information from a wide range of sources through an evaluation of the feasibility of adaptation technologies for the agricultural sector in Sri Lanka.Employing the multi-dimensional feasibility assessment approach, I scored seven adaptation technology groupings across six dimensions: technological, economic, geophysical, environmental, institutional, and social-cultural.Scores were assessed with the peer-reviewed literature and then using grey literature.Using the peer-reviewed literature, many adaptation options had moderate feasibility on many dimensions, but with significant gaps regarding institutional and social considerations.The grey literature addressed some gaps, although this introduced an additional effort to establish their credibility and potential biases in their reporting.This work concludes with recommendations for assessments of climate technologies in data-sparse contexts.
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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 | 0.232 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".