Bibliographic record
Abstract
Abstract Geologists whose research deals with environmental problems such as landslides, floods, earthquakes and other natural hazards that affect people's health and safety must communicate their results effectively to the public, policy-makers and politicians. There are many examples of geological studies being ignored in policy and public action; this is in due in part to geoscientists being poor communicators. Scientists often use complicated and difficult to understand language, talk mostly to other scientists, and are not trained to work with the media. They generally are not encouraged by their employers and funding agencies to communicate to non-scientists. Environmental geoscientists must make their research publications more accessible to the public by including plain-language summaries. They should work with media and communications professionals, and seek training in how to communicate better. They need to understand the different approaches that will work with different audiences. Universities, employers and funding agencies should encourage environmental geoscientists to improve communication skills, and to reward attempts to explain their research to non-scientists.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".