Data and script for: Trends and drivers of Arctic lake color change from Landsat time series"
Bibliographic record
Abstract
Overview This repository contains the information for the paper "Trend and drivers of Arctic lake color change from Landsat time series". DOI: (to be added after publication) Data All datasets used in this study are publicly accessible, including: USGS Landsat 5, 7, 8 Level 2 Collection 2 Tier 1 archives, accessed through Google Earth Engine; HydroLAKES v1.0 (Messager et al. 2016); Dataset for Assessing volumetric change distributions and scaling relations of thaw slumps across the Arctic (Bernhard 2021); CRU TS v4.04 (Harris et al. 2020) Scripts The Google Earth Engine (GEE) scripts used for applying cross-sensor Landsat calibration and constructing lake-level Landsat time series can be accessed on https://code.earthengine.google.com/?accept_repo=users/Qianyu_Chang/lakecolor This repository contains Python scripts for: calibration.py - computing Landsat cross-sensor calibration coefficients based on matchup records produced using the GEE script. trends.py - computing gradual lake color change. beast.py - detecting abrupt changes in lake color with the BEAST changepoint analysis; linking abrupt changes to inter-annual summer temperature variation using lagged correlation function. fire.py - computing fire impact on lake color. A more detailed description of each script can be found within the .py file. The .csv files are associated with the Python scripts, including: HydroLakes.csv - HydroLAKES metadata: ID, number of lakeshore slumps, sub-region, and fire label of each lake. lakeAnnualStats.csv - annual mean summer lake color of each lake measured by surface reflectance in red, green, and NDTI. lakeTrends_1985-2022.csv - Sen's slope of each lake and each color metric during 1985 - 2022. calibrationMatchup_78.csv - sample Landsat 7/8 matching observations used for cross-sensor calibration, computed on Google Earth Engine. References Bernhard, P., 2021. Dataset for Assessing volumetric change distributions and scaling relations of thaw slumps across the Arctic. https://doi.org/10.3929/ETHZ-B-000482449. Harris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3. Messager, M.L., Lehner, B., Grill, G., Nedeva, I., Schmitt, O. (2016). Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7: 13603. https://doi.org/10.1038/ncomms13603.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.186 | 0.132 |
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".