Collective intelligence for addressing community planetary health resulting from salinity prompted by sea level rise
Bibliographic record
Abstract
Sea level rise-induced salinity encroachment is causing various community-level planetary health impacts in coastal areas worldwide. The coastal area of Bangladesh is no exception. Driven by sea level rise, coastal Bangladesh's salinity is amplified by other factors such as shrimp cultivation, reduction of transboundary river flow in the dry season, mismanagement of the embankment, and frequent cyclone-related storm surges. Due to the salinity encroachment in this region, water and soil salinity is increasing, resulting in multiple planetary health impacts. Based on twenty years of field observation and an extensive literature review, these health impacts can be categorized as (i) primary health consequences (communicable and non-communicable diseases; scarcity of potable water), (ii) secondary health consequences (food and nutrition security; migration and related health impacts) and (iii) tertiary health consequences (adaptation-related emerging diseases; disaster-related health vulnerability). By exploring these multidimensional health impacts and associated factors of salinity, a collective intelligence-based framework to address the health impacts is described in this paper. Collective intelligence can be a valuable technique to engage multiple stakeholders in sharing and gathering data, and to facilitate the modeling of the health impacts of salinity. Collective intelligence can also help indicate appropriate interventions to address the planetary health impacts of increasing salinity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".