Quantifying Distribution in Carbon Uptake and Environmental Measurements with the Gini Coefficient
Bibliographic record
Abstract
The Gini coefficient is a measure used in economics to evaluate the equitability of the distribution of a resource across a population. This project applied the Gini coefficient as a classification method for a decade-long data set consisting of environmental observations and carbon flux data for a coniferous forest in Finland. Our results show consistency in the Gini coefficient for environmental variables, even with interannual variation in the measurements during the carbon uptake period or when the ecosystem is absorbing carbon from the atmosphere. The Gini coefficient calculations showed this ecosystem has an inequitable distribution of carbon uptake and release within the carbon uptake period, which is comparable to the inequitable distribution of temperature and precipitation during the same time period. We also calculated the percentage of the carbon uptake period that has passed for different cumulative proportions of a measurement. Future applications of the Gini coefficient to other ecosystems will enhance knowledge of the distribution of environmental and flux measurements across the carbon uptake period.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".