Environmental forensics: Where techniques and technologies enforce safety and security programs
Bibliographic record
Abstract
Safety and security engineering involve several and complex multidisciplinary activities aimed to preserve people and the environment from hazards and risks. These concepts that were born as "umbrella" terms for the laws, rules; process design is generally applied only for workplaces or related to the employees; indeed, the scenario of related applications is only limited by the typical needs of the specific context. Recently, new methods and applications for detecting, evaluating, and tracking signs of environmental contamination are validating the effectiveness of safety and security engineering tools also in the environmental field. As in a workplace an engineer is called to analyze a complex scenario (e.g. to evaluate a risk, to assess a real danger and, therefore, look for causes to define the dynamics and find a solution), in the same way the environmental forensic scientist has to examine scenarios and actors to define the relationships to reveal source, path and target; in both the same techniques and technologies used in the analyses play a key role. This paper introduces a multidisciplinary strategy that bridges different approaches incorporating remote/proximal sensing applications where techniques and technologies enforce safety and security programs. A part of Campania coast, close to Salerno city in southwestern Italy, was chosen as a test bed of our strategy. All the activities were performed supporting the environmental investigations directed by Salerno Prosecutor Office and also cooperating with Italian police and several Government bodies. This paper provides an example where law enforcement and university research teams collaborate to develop enhanced environmental protection methods.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".