Utilization of Virtual Globes for Open Source Industrial Symbiosis
Bibliographic record
Abstract
Several proprietary industrial symbiosis software tools have been developed, however, without long term financial commitments these endeavors fall short of creating generalizable and sustainable tools.The recent development of virtual globes such as Google Earth (GE), an information service that provides imagery and three dimensional data depicting the entire physical earth, provides an opportunity to use a new sustainable method of navigating information to save energy and use materials more efficiently in the real world.To test this open source methodology, a data set was used from the Department of Environmental Protection in Pennsylvania, which has mandated reporting the location of disposal and type of residual waste from sources producing more than one ton per month.This data set was integrated into the GE interface to identify and quantify opportunities for materials and energy efficiency improvements.This investigation found that virtual globes coupled with open source waste information can be used to: 1) reduce embodied transport energy by reducing distances to recycling facilities, 2) choose end of life at recycling facilities rather than landfills, and 3) establish industrial symbiosis and eco-industrial parks on known by-product synergies.For one waste category, a potential 70% reduction in ton-kilometers was identified by simply observing closer disposal locations than currently utilized; similar reductions are achievable in other categories.It is concluded that the open source sharing of information in virtual globes provide a means to identify economically and environmentally beneficial opportunities for waste management if the data have been made available.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".