The Serious Flaws of the Academies’ Report on Weather Modification Research
Bibliographic record
Abstract
The Report “Critical Issues in Weather Modification Research” by the National Research Council of the National Academies (the Report, in short) stresses lack of physical understanding as the reason for rejecting all success claims in modification (involving convective clouds). This conclusion is reached without realizing that statistics provides the only scientific tool to link the physics of clouds to the interference of seeding; it weighs the quality of the basic physical hypotheses underlying the seeding. Yet statistics, the cornerstone in the assessment of weather modification experiments, is not even discussed in the bulk of the Report. The faults in the Executive Summary are easily spotted. The main body of the Report, however, is well written and its even bigger flaws are hidden in beautiful prose. The main failings of the Report will be described below.
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 | 0.107 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".