Photosynthesis and hydrogen energy for sustainability: harnessing the sun for a greener future
Bibliographic record
Abstract
At the dawn of the 21st century, the rapid expansion of manufacturing plants and the widespread destruction of natural habitats significantly contributed to accelerating global warming. This phenomenon has led to severe droughts, irreversible agricultural damage, and substantial challenges in securing food supplies for the burgeoning global population. The alarming surge in atmospheric carbon dioxide concentrations underscores the urgent need to embrace clean energy technologies. To date, the primary goal of mankind is to develop innovative approaches to return Earth's ecology to its pre-industrial condition, as a century ago. The special issue (SI) in the International Journal of Hydrogen Energy presents a collection of papers on photosynthetic and biomimetic hydrogen (H2) production, presented at the 'Photosynthesis and Hydrogen Energy Research for Sustainability - 2023' conference, held in Istanbul, Turkey, from 3-9 July 2023 (https://phrs-conference.com). The event was supported by the International Society of Photosynthesis Research (ISPR) and the International Association for Hydrogen Energy (IAHE). SI aims to deliver the latest insights into sustainable energy, with a particular emphasis on Biohydrogen and Artificial Photosynthesis. At the conference, nine promising young investigators were honoured with awards. Included herein are photographs capturing the conference's congenial atmosphere. We cordially invite you to the 12th International Meeting of 'Photosynthesis and Hydrogen Energy Research for Sustainability - 2024', honouring esteemed researchers John Allen (UK), Eva-Mari Aro (Finland), Ibrahim Dincer (Canada), Kazunari Domen (Japan), Elizabeth Gantt (USA), Andrey Rubin (Russia), and scheduled to take place in Turkey (13-19 October 2024).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".