Supporting Data for Physical Modeling of Coastal Permafrost Erosion: A New Model for Predicting Niche Depth Evolution
Bibliographic record
Abstract
The contents of this folder are uploaded in support of the manuscript titled "Physical Modeling of Coastal Permafrost Erosion: A New Model for Predicting Niche Depth Evolution" submitted to the Journal of Geophysical Research-Earth Surface (April, 2025). The experimental study on coastal permafrost erosion was conducted at Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, 490 Rue de la Couronne, Québec City, QC G1K 9A9, Canada. Users should note this mapping when referencing the "Test_ID" or "Test_number" between the main text and the data:test_id_mapping = { 'Test_00': 'A12(0-0)', 'Test_01': 'B12(0-0)', 'Test_02': 'C12(0.02-0.8)', 'Test_03': 'C12(0.03-0.8)', 'Test_04': 'C12(0.04-0.8)', 'Test_05': 'C12(0.02-1.0)', 'Test_06': 'C12(0.03-1.0)', 'Test_07': 'C12(0.04-1.0)', 'Test_08': 'C12(0.02-1.2)', 'Test_09': 'C12(0.03-1.2)', 'Test_10': 'C12(0.04-1.2)', 'Test_11': 'D15(0.04-0.8)' } image_processing: contains the image digitized path from which the erosion rates were calculated as described in section 3.4.3 of the original manuscriptTemperature_data: contains the raw temperature data recorded during the experiments. Channels 1 to 8 corresponds to the Temparture sensors Tp1 -Tp8, respectively.Wave_files: contains the raw wave data recorded by the wave gauges WG1 to WG8 during the experiments. The columns corresponds to WG1- WG8 respectively Enquiries can be directed to the corresponding author.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.775 | 0.304 |
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".