Ancestral Human-Water Feedbacks Help on New Regional Models of Anthropogenic Effects and Interactions with Local Communities
Bibliographic record
Abstract
We state Ancestral Human-Water Feedbacks (AHWF) into derived regional models of anthropogenic effects and interactions with local communities. On the one hand, we revisit alternative AHWF models from Ailton Krenak’s ancestral future perspectives, quoted for the value of history in global hydrological paradigms (Beven et al, 2025) and even enhanced into hydrological heritage living with droughts (i.e. Pereira et al, 2025). On the other hand, we adapt AHWF models for regional scales from both non-formal cosmogony (e.g. Apgar et al, 2009) and externalist perspectives on metacognition (i.e. from Arfini & Magnani’s, 2022). Thus, the AHWF puts concepts of “knowledge”, “information” and “belief” into practice. In this AHWF, new “embodied”, “extended” and “distributed” anthropogenic effects, with novel sociohydrological archetypes, are theoretically modeled. To conceptualize and simulate feedbacks in human water systems, this AHWF is applied for the coevolution of the Center of Water Resources and Environ. Studies (CRHEA) in Cerrado Biome, Brazil, with river-lake-hydropower-urban settlements. Therefore, connections to regional biomes like the Amazon and the Atlantic Forest are possible to include in this AHWF model through the support of the DREAMS project (‘Flash DRought Event evolution chAracteristics and the response Mechanism to climate change considering the Spatial correlations). Moreover, the AHWF is now operationalised with the SOPHIE initiative (Sustainable Observatory of Planetary Health through Innovation and Entrepreneurship”), with the possibility of the creation of databases for future digital twins and serious games. Topical applications of this AHWF model range for all IPCC-climate impact-drivers and their composite risks (i.e. planetary health, agri-food systems, climate change, water security, biodiversity losses, etc.) with focus on adaptation to hydrological extremes like floods, droughts and water scarcity. Future works are envisaged for the co-alignment of legacies of the IAHS-HELPING Science Decade, the WMO Early Warnings for All initiative, the UNESCO-IHP-IX Strategic Plan, the IWA Digital Water Program and the UNEP World Water Quality Alliance.References: Apgar et al (2009) Intl. J. Interdiscipl. Soc. Sci., https://doi.org./10.18848/1833-1882/CGP/v04i05/52925; Arfini, S., Magnani, L., 2022, https://doi.org/10.1007/978-3-031-01922-7; Beven et al, 2025, Hydrol. Sci. J., https://doi.org/10.1080/02626667.2025.2452357; Mendiondo, E M (2023) DREAMS Project, FAPESP 22/08468-0, https://bv.fapesp.br/en/auxilios/111385/flash-drought-event-evolution-characteristics-and-the-response-mechanism-to-climate-change-consideri/ ; Pereira et al, 2025, Hydrol. Sci. J., https://doi.org/10.1080/02626667.2024.2446272
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".