Climate change in pictures photo competition winners announced
Bibliographic record
Abstract
On behalf of the ICTWCC project management team, please accept our heartiest appreciation to all of you for participating in the photo competition. \nClimate Change is considered to be one of the most serious threats to sustainable development, with adverse impacts on all developmental sectors including agriculture. A significant body of evidence points to developing nations in Africa, Asia and Latin America as the most vulnerable regions to climate change. \nThe ICTWCC project offers scholarship to 31 grantees from Africa, Asia and Latin America & Caribbean. The grantees focus their study on the global impacts of Water and Climate Change, with ICT as the mode of adaptation or mitigation. The project is jointly coordinated by the School of Computing & Informatics (SCI) and LARMAT of the University of Nairobi. It is funded by IDRC, Canada. \nAs the adage goes, a picture is worth a thousand words, ICTWCC project had run a photo competition. The photo submissions were made by the project grantees; source of pictures being their field research. \nIt has been an interesting journey from egging the grantees to send the submissions, and then asking them to receive votes for their submissions. \nThe most exciting part was counting the number of increasing votes on a daily basis. The grantees and the team have all worked hard to spread the word and gain a vote for the respective submissions. \nSo the time has now arrived to announce the winners!!!!! \nThe winners are listed on our website. Visit http://t.co/n72Vt3F5Zs to see the proud winners. \nLook out for our soon to be launched Paper writing Competition. \nHeartiest congratulations to our winners.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.297 | 0.091 |
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".