Bibliographic record
Abstract
Lightning is one of nature's most beautiful and awesome sights. Yet it can also be extremely dangerous, presenting a major natural hazard in many different environments, from power utility companies to civil aviation, to golfers, and more. Thousands of people are killed every year by lightning bolts, while tens of thousands are injured (Cooray et al., 2007). Lightning impacts both our daily commercial and recreational activities. In the United States alone, damages due to lightning strikes amount to tens of millions of dollars annually (Curran et al., 2000). In recent years, with great interest in renewable energy, wind turbines have become extremely vulnerable to lightning damage (Glushakow, 2007). Furthermore, most commercial airliners are struck about once a year by lightning; however, due to the protective metal skin, generally little damage is incurred. Tens of thousands of fires are also ignited by lightning every year, generally in temperate or high latitudes (e.g., Canada, Siberia, etc.) (Stocks et al., 2002). In such cases, tens of fires can be ignited locally on the same day as a storm passes through, causing major problems for fire crews and fire management. Hence, knowledge of how lightning activity may change as the Earth's temperature changes is of critical importance and interest.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".