Building synergies in regional climate services for Southeast Asia: The ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series.
Bibliographic record
Abstract
Southeast Asia (SEA) is a rapidly developing and densely populated region that is home to over 600 million people. This, together with the region’s high sensitivity, exposure and low adaptive capacities, makes it particularly vulnerable to climate change and extremes such as floods, droughts and tropical cyclones. While the last decade saw some countries in SEA develop their own climate change projections, studies were largely uncoordinated and most countries still lack the capability to independently produce robust future climate information. Following a proposal from the World Meteorological Organisation (WMO) Regional Association (RA) V working group on climate services, the ASEAN Regional Climate Data, Analysis and Projections (ARCDAP) workshop series was conceived in 2017 to bridge these gaps in regional synergies. The ARCDAP series has been organised annually since 2018 by the ASEAN Specialised Meteorological Centre (hosted by Meteorological Service Singapore) with support from WMO through the Canada-funded Climate Risk and Early Warning Systems (Canada-CREWS) initiative. This presentation will cover the activities and outcomes from the first two workshops, as well as the third which will be held in February 2020. The ARCDAP series has so far brought together representatives from ASEAN National Meteorological and Hydrological Services (NMHSs), climate scientists and end-users from policy-making and a variety of vulnerability and impact assessment (VIA) sectors, to discuss and identify best practices regarding the delivery of climate change information, data usage and management, advancing the science etc. Notable outputs include two comprehensive workshop reports and a significant regional contribution to the HadEX3 global land in-situ-based dataset of temperature and precipitation extremes, motivated by work done with the ClimPACT2 software. The upcoming third workshop will endeavour to encourage the uptake of the latest ensemble of climate simulations from the Coupled Model Intercomparison Project (CMIP6) using CMIP-endorsed tools such as ESMValTool. This will address the need for ASEAN climate change practitioners to upgrade their knowledge of the latest global climate model database. It is anticipated that with continued support from WMO, the series will continue with the Fourth workshop targeting the assessment of downscaling experiments in 2021.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".