Semantic Workflows and Machine Learning for the Assessment of Carbon\n Storage by Urban Trees
Bibliographic record
Abstract
Climate science is critical for understanding both the causes and\nconsequences of changes in global temperatures and has become imperative for\ndecisive policy-making. However, climate science studies commonly require\naddressing complex interoperability issues between data, software, and\nexperimental approaches from multiple fields. Scientific workflow systems\nprovide unparalleled advantages to address these issues, including\nreproducibility of experiments, provenance capture, software reusability and\nknowledge sharing. In this paper, we introduce a novel workflow with a series\nof connected components to perform spatial data preparation, classification of\nsatellite imagery with machine learning algorithms, and assessment of carbon\nstored by urban trees. To the best of our knowledge, this is the first study\nthat estimates carbon storage for a region in Africa following the guidelines\nfrom the Intergovernmental Panel on Climate Change (IPCC).\n
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| 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".