Monitoring of Exposure to and Potential Effects of Contaminants in the Environment
Bibliographic record
Abstract
In our lifetimes, much of what was once considered science fiction: space ships, monitoring the environment from space, satellite phones and biomedical advances in the diagnosis and treatment of disease at the molecular level have now become realities. These advances in technology have changed our perceptions and how we interact with the world around us. Also during this time, the world population has doubled several times, our use of natural resources has increased exponentially and we continue to release natural and synthetic compounds to the global environment at an ever-increasing rate. These actions have altered the face of the planet in a multitude of ways. In the ultimate irony, technology has proven to be a double-edged sword that can both threaten to destroy us while also possibly providing the means of our salvation, our means of achieving sustainable development. How can we harness the rapidly developing technological means at our disposal to predict and manage environmental changes? There are exciting changes in technology that may provide the ability to monitor the environment, providing the information we need to allow us to make wise environmental policy decisions. However, even with these advances in technology, the basic dilemma still facing environmental scientists is complexity of ecosystems where known and unknown natural and anthropogenic factors may adversely influence natural processes, resulting in degradation of living resources, environmental services, and human health. Conversely, failure to effectively monitor will lead to our failure to detect threats to human health and reductions in biodiversity, resulting in higher costs associated with after-the-fact remediation and restoration, with the ultimate risk of irreversible damage to environmental resources. Within this context, it is important that as environmental scientists, we provide the necessary information to the general public and regulatory stakeholders that is readily interpretable and is related to valued resources and functions of ecosystems, allowing these stakeholder groups to make informed and effective resource-management decisions in real-time. Efforts are still needed to develop programs to detect, monitor and assess impacts in bio-diverse ecosystems by measuring the right things, in the right places, at the right frequency over sufficient time periods in a cost-effective manner. To increase the efficiency and effectiveness of monitoring programs focusing on “known” chemicals and the potential effects and risks they may pose to biota, including humans, we propose that risk-based monitoring programs be developed incorporating chemical and biological techniques that are rapid, readily implemented, and provide
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".