„Good” and „Bad” Ozone - Evaluation on the Basis of Plant Reaction to Ozone / „Dobry” I „Zły” Ozon - Ocena Na Podstawie Reakcji Roślin Na Ozon
Bibliographic record
Abstract
Abstract Ozone is a natural and artificial chemical compound of Earth’s atmosphere. O3 is an absorbent of ultraviolet and infrared radiation and has strong oxidative properties. In the stratosphere the ozone layer protects the planet’s surface from dangerous UV radiation, its indirect effect on plant organisms is positive so stratospheric ozone can be called “good” ozone. The depletion of the ozone layer, as a result of atmosphere pollution, described as an ozone “hole” is causing UVB radiation enhanced level on Earth’s surface. The genetic, cytological, physiological and morphological reaction of prolonged UVB exposure in plants is twofold: it damages plants and simultaneously plants protect themselves and repair their injuries. The ozone in the troposphere originates from natural sources and is also a secondary pollutant, formed in photochemical reactions, leading to “smog” and ozone “spots” occurrence. As a strong oxidant, O3 is directly toxic to plants and can be recognized as “bad” ozone. Ozone is also classified as a “greenhouse” gas, participating in global warming. It is difficult to value the impact of O3 as a “greenhouse” gas on plants. The combined effect of O3 changes in the stratosphere and troposphere on plants can be estimated as loss in crop yield and in productivity of natural ecosystems
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".