Characterizing Methane Emission Response to the Past 60 Years of Permafrost Thaw in Thermokarst Lakes
Bibliographic record
Abstract
In this NASA ABoVE-funded project, we combine geospatial data products derived from airborne and spaceborne remote sensing (RS) data with targeted field observations and modeling in order to quantify ecosystem responses to Arctic and boreal environmental change. Specifically, we quantify methane (CH4) ebullition (bubbling) emissions associated with 60 years of permafrost thaw in thousands of Alaskan and NW Canadian lakes by direct observation with RS systems. \n \nTo achieve our goals, we have developed statistically-significant models that are using SAR, optical and infrared RS data in order to detect and quantify CH4 ebullition emissions at intra-, whole- and regional-lake scales. We also established a relationship between observed CH4 ebullition and average annual soil organic carbon (SOC) inputs to a handful of Alaskan lakes via thermokarst-margin expansion during recent decades using field data, radiocarbon dating and modeling. \n \nOur paper we will provide an overview of the goals, datasets, and methods used for the various components of this project. We will present on (1) the collection of new and synthesis of existing field data on CH4 ebullition, thaw-bulbs and SOC; (2) the analysis of existing data from aerial surveys, SAR and optical RS of CH4 in lake ice; (3) the orthorectification of historic aerial photos for comparison to high-resolution satellite imagery to produce fine-scale regional maps of lake area change, (4) the modelling of permafrost SOC quantities eroded into lakes; (5) the radiocarbon dating of CH4 and SOC, (6) GIS modeling to produce multi-temporal regional maps of historic lake area change, associated CH4 emissions, and permafrost SOC stocks; and (7) outreach to stakeholders at Alaska village and rural community field sites. To demonstrate the scientific relevance of our work we will also showcase a set of research results that we have been able to achieve so far. These will include (1) first regional-scale RS-based estimates of lake-borne CH4 ebullition emissions; (2) regional scale estimates of lake area change from an analysis of 50 years of remote sensing data; and (3) regression models linking lake area change to CH4 emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".