PP338 Topic: AS09–Global Health/Resource Limited Setting/Health Inequalities/Impact of Global Warming/Other: IMPACT OF IN-PERSON VS VIRTUAL ATTENDANCE ON THE CARBON FOOTPRINT OF CONSENSUS CONFERENCES: COMPARISON OF PALICC AND PALICC-2
Bibliographic record
Abstract
Aims & Objectives: Limited research exists on the comparative impact of consensus conferences held in different formats (in-person versus virtual attendance). This study seeks to examine and compare the carbon footprint between PALICC (Pediatric Acute Lung Injury Consensus Conference, 2012-2015, conducted in person) and PALICC-2 (2020-2022, predominantly virtual). The study also investigates the influence of these formats on the quality of interactions, which is reported in a separate abstract Methods: Utilizing a validated questionnaire, we collected data regarding the attendance type and travel information of each expert for every conference segment. Carbon footprint computations were conducted employing the Ecoinvent database and ADEME’s calculator, encompassing both travel-related emissions and those associated with web conferences Results: The response rate was 84% (n=49), and travel data from 9 non-respondents were estimated based on their affiliation location. PALICC convened in Chicago, Montreal, and Paris, emitting a total of 92 tons of CO2 equivalent (average 3.4 tons per participant). PALICC-2 comprised 38 hours of web conferences and a hybrid meeting in San Diego, resulting in 13 tons of CO2 equivalent (average 0.2 tons per participant) Conclusions: Despite the often-underestimated impact of virtual conferences, PALICC-2’s predominantly virtual format yielded a carbon footprint approximately 7 times smaller than that of PALICC. This advantage warrants further examination in light of its potential impact on interpersonal interactions, currently studied in parallel. Implementing solutions to facilitate consensus conferences while aligning with the COP21-defined target of limiting individual carbon emissions to 2 tons per year by 2050 is imperative Keywords: environmental impact, consensus conference
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.214 | 0.028 |
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".