Assessment of crop losses from ozone using biomonitor plants and risk estimates by experts
Bibliographic record
Abstract
Environmental policy makers are required to make decisions under uncertainty regarding the benefits and costs of specific regulatory action. Uncertainty is a phenomenon that cannot be avoided in the assessment of environmental impacts, due to the inherent stochasticity of environmental systems, as well as a lack of adequate empirical data related to specific cause and effect relationships. A primary constraint associated with generation of adequate data from experiments is that environmental research is expensive, and conclusive results may take several years to obtain. In the meantime, significant impacts could be occurring, virtually undetected. A high degree of uncertainty exists in the assessment of the potential effects of ozone (O₃) pollution on agricultural crop yield. Thus, the purpose of this research was to provide information related to the potential impacts of O₃ pollution on crops in the Fraser Valley east of Vancouver, British Columbia, Canada. Two alternate methods were utilized: (i) biomonitoring with Bel W-3 tobacco, a plant variety that is very sensitive to O₃, and (ii) expert judgments of the risks of crop losses from O₃. The biomonitor survey was conducted over three growing seasons (1985 - 1987), in which ambient O₃ pollution conditions were atypically low, limiting the injury response data obtained. However, a correlation was established between biomonitor injury response and ambient O₃ levels, demonstrating that phytotoxic pollution conditions occurred during these years. Calibration of biomonitor injury response with crop yield losses revealed the following: (i) yield losses due to O₃ exposure are likely in the event that biomonitor plants exhibit O₃-induced injury symptoms, and (ii) the absence of biomonitor injury does not preclude the possibility of crop loss, since the O₃ exposure threshold for biomonitor injury may exceed that for loss of certain crops. Although experts are commonly used to provide judgments of potential impacts under uncertainty, there is a paucity of information regarding the desirable attributes of expertise. Selection of experts is largely an ambiguous task, and choices of experts by different persons are likely to be inconsistent. Prior to selection of experts for this project, a comprehensive survey was conducted of over 200 environmental professionals to determine the characteristics of an expert in O₃ effects on crops. It was shown that expertise in this area involves a considerable number of attributes. These were grouped, using factor analysis, into seven independent dimensions: education, type of career experience, length of career experience, cognitive skills, personal qualities related to credibility, scientific recognition and involvement in the scientific community. In general, there was agreement between different groups (e.g., research scientists and members of conservation groups) regarding the relative importance of the various dimensions of expertise. Nine crop loss experts were selected, based on nomination by a large group (166) of their scientific peers. It was demonstrated with regression analysis that nominated experts exhibited the attributes identified in the survey. Logit models were estimated that predict an individual's degree of expertise in O₃ effects on crops, based on specific attributes possessed by that individual. Independent judgments were then obtained from the nine experts regarding probable crop losses under typical O₃ pollution conditions in the Fraser Valley. Probabilistic judgments of crop losses were generally similar among experts and approximated the level of crop losses predicted from the biomonitor survey. Limited empirical exposure-response information for Fraser Valley crops indicated that some cultivars may be more sensitive than assumed by the experts. Additional exposure-response experiments will be required to determine the source of this inconsistency.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".