Progress in the study of typical planetary landforms over the past 30 years: a bibliometric analysis in CiteSpace
Bibliographic record
Abstract
Landform investigation reports are fundamental datasets for ecology, geology, and geography as they record the morphological traces of planetary surface processes, which have been the focus of top journals and distinguished scholars. Investigating landforms on Earth and other planetary bodies is crucial for understanding planetary formation and geological evolution. Remote sensing techniques have proven to be powerful tools for studying terrestrial and planetary landforms, providing detailed insights into the interactions between land surfaces and both endogenic and exogenic forces and the mechanisms driving physical processes under different conditions. This study focuses on the remote sensing perspective of planetary landform investigations, highlighting recent progress in geomorphic research. Publications in top-tier journals over the last 30 years were selected for analysis. Our findings indicate that geological history and the environmental conditions conducive to the survival, growth, and daily activities of living organisms are of great concern. The United States, the European Union, China, Japan, and Canada, along with their aerospace research institutions, continue to be key research centers and play important roles all the time. Moreover, our literature survey reveals that research on planetary landforms is gaining momentum in more countries because of advancements in instrumentation and increased international collaboration. Celestial bodies that receive significant attention include planets, moons, and comets, while asteroids should receive more attention in the future. To advance our understanding of planetary landforms, higher-precision observation data and more refined simulations are essential. Continued improvements in international cooperation will be crucial for unraveling the complex geological histories within planetary landforms, contributing to our broader understanding of planetary evolution within the solar system.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.039 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".