27th Annual Mersivity / Water-HCI Symposium
Bibliographic record
Abstract
This Proceedings presents a collection of papers and other research associated with the 27th Annual Mersivity / Water-HCI Symposium held at HTO Beach, which is Toronto's last remaining and only accessible beach (i.e. in which a wheelchair can come all the way to the water's edge). Mersivity is socio-cyber-physical technology that connect us to each other and to our surroundings (environment), i.e. XR / XI / XIR (eXtended Reality, eXtended Intelligence, eXtended Intelligent Reality) for humanity and Earth, where XI (or equivalently XR, XIR) is not merely a broad umbrella term to interpolate between the “alphabet soup” of the realities (VR, AR, MR, DR, etc.) and Intelligences/Internets (AI, spatial computing, Internet of Things, Digital Twin, etc.), but, more importantly, to eXtrapolate beyond them. Mersivity embodies wearable AI, physical AI, sustainable AI, and the like, in service of us and our world, as well as “cyborg” technologies and technological prostheses. Many examples of Mersivity involve adaptive technology that helps individuals, thus accessibility is an important element of Mersivity. Mersivity is at the nexus of the physical world of "atoms", the cyber world of "bits", and the social world of "genes". WaterHCI is the branch of Mersivity that focuses on the physical world with an emphasis on water, water stewardship, water conservation, and, most notably, water-human-computer interaction, and, more generally water-human-technology interaction. Water-HCI is particularly relevant because the world’s earliest example of “cyborg” technology is the vessel (e.g. “boat”, raft, paddleboard, etc.), an invention that predates the invention of the wheel, the invention of clothing, and even homo-sapiens. Vessels represent an example of the broader class of technologies known as “vironments” that separate our “invironment” (us as individuals or groups) from our environment (our surroundings). Vironmentalism is the study, science, engineering, practice, and art of clothes, vehicles, vessels, and the like, which is integral to the concept of Mersivity.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.157 |
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".