Comparison of Methods for Estimation of Kyoto Protocol Products of Forests From
Bibliographic record
Abstract
Abstract- The Kyoto Protocol requires nations to report on their reforestation, afforestation, and deforestation (RAD). Using 1990 as a baseline, nations are also required to monitor changes in carbon stocks leading up to the reporting period 2008 to 2012. A study was conducted using three dates of summer Landsat 5 imagery to estimate above-ground carbon for a forested test site near Hinton, Alberta [1]. The carbon estimates were compared with those derived from Canada’s national forest inventory. The remote sensing estimates for areas that had not changed were consistent year to year within 3%. The experiment was repeated with the addition of leaf-on and leaf-off image pairs and Landsat-7 imagery. A comparison was made of the classification accuracies achieved for forest classes with single date and paired leaf-on and leaf-off image sets. Spatial properties were incorporated into the image analysis by first creating a multitemporal segmentation [2]. This paper reports on the classification methods used, compares the classification accuracies achieved, and gives recommendations for the creation of Kyoto Protocol products for temperate forests derived from remotely sensed imagery. I.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".