Photon Non‑Conversion Energy Accumulation Hypothesis (PNEAH)
Bibliographic record
Abstract
This study proposes the Photon Non‑Conversion Energy Accumulation Hypothesis (PNEAH), a novel framework that identifies and quantifies the portion of incoming solar energy that fails to be emitted as photons and instead remains as heat within the near‑surface atmosphere. Under conditions of strong direct insolation, low wind speed, and reduced radiative loss, this residual energy accumulates rapidly, producing extreme and sustained high temperatures even without meteorological phenomena such as the Foehn effect. The PNEAH is formulated through an energy balance model: Ein=Eγ+Eheat,ηγ=EγEin,dHdt=Eheat−L,ΔT=HρcpE_{\text{in}} = E_{\gamma} + E_{\text{heat}}, \quad \eta_{\gamma} = \frac{E_{\gamma}}{E_{\text{in}}}, \quad \frac{dH}{dt} = E_{\text{heat}} - L, \quad \Delta T = \frac{H}{\rho c_p}Ein=Eγ+Eheat,ηγ=EinEγ,dtdH=Eheat−L,ΔT=ρcpH where ηγ\eta_\gammaηγ is photon conversion efficiency, EinE_{\text{in}}Ein is incoming solar energy, EheatE_{\text{heat}}Eheat is retained thermal energy, and LLL is the heat loss rate. Application of this model to extreme heat events — including Isesaki, Japan (2025), Death Valley, USA, Cambridge, UK, and Lytton, Canada — shows consistent explanatory power across diverse climates. Even a 3–7% reduction in photon conversion efficiency corresponds to tens of W/m² in additional retained heat flux, sufficient to account for observed temperature anomalies. The paper also outlines mitigation and adaptation strategies to increase photon conversion efficiency and reduce heat retention in urban environments, offering practical pathways for managing extreme heat risks.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".