The Effects of Temporality and Spatiality Due to Aged Data in LIfe Cycle Assessments
Bibliographic record
Abstract
Life cycle assessment is a relatively new—although decades old—method for assessing the environmental impacts of goods and services. It seeks to quantify these impacts in such a manner as to facilitate informed decisions regarding different, yet equally viable, options. However, this aim must be conditional on the notion that these impacts are measured with a number of associated qualifications or caveats, two of which is subject of this work. As subject matter, temporality and spatiality in life cycle assessment are both very broad, although this dissertation focuses specifically on temporality and spatiality due to age of data. The structure of the dissertation follows three distinct phases. The first phase contextualized the subject matter and its relation towards standardization of life cycle assessment methods. In doing so, it identifies and contextualizes the subject matter. The second phase identified Greenhouse gases, Regulatory Emissions, and Energy use in Transportation 2 as an ideal model on which to assess temporality and spatiality due to age of data since it models the life cycle assessment of an assortment of different vehicles. This phase also involved data collection, and uses a platform of assessment tools including Monte Carlo simulations, analysis of variance, F tests, regression analysis, and tests for non-normality (kurtosis and skewness). Building on the second phase, the third phase moved beyond the original phases by more than doubling the amount of materials of manufacture to be studied and adding further tools for assessment, the mainstay of which are regression analyses. Overall, this study found that the use of Monte Carlo simulations and analysis of variance are useful for identifying long term variation in energy intensity of materials. F-tests were useful in identifying which materials showed effects owing to spatiality. Although not in all instances, tests for non-normality identified which circumstances merit log transformation to bring about more accurate results. Linear regression techniques were used as a posterior test to confirm the origins of the variation seen in the Monte Carlo simulations and the analysis of variation. Moving ahead, this study pointed to the need for more concerted efforts in data promulgation.
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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 | 0.226 | 0.531 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".