Steady theme

lst smap

One of 50 themes found in 20,416 EGU water abstracts (2024 to 2026). Its most distinctive words are below — they are what separates this body of work from the rest of the corpus.

lstsmapvodssmmicrowaveismn

500

abstracts in this theme

2.4% of the water corpus · 11 of 50 by size

16.8%

use machine learning

3.6 points above the 13.2% corpus average

-0.06

points of share, 2024 to 2026

29 of 50 by trend

of 50 for AI penetration

mid-pack on AI adoption

How to read this theme

This theme holds 500 of the 20,416 water abstracts, making it the 11th-largest of the 50 themes. Between 2024 and 2026 its share barely moved, by -0.06 points.

16.8% of its abstracts were flagged as using machine learning, 3.6 points above the 13.2% corpus average. AI adoption here sits close to the corpus norm.

Find data for this theme

Search the directory for datasets matching this theme's defining terms.

lst smap vod ssm microwave ismn

Semantically nearest themes

Themes that sit closest to this one on the semantic map — the work that reads most like it.

wheat maize

wheat, maize, farmers, rice, rainfed, soybean

481 abstracts · 9% use AI · -0.91 pp

gpp vpd

gpp, vpd, nee, deforestation, lai, leaf

634 abstracts · 10% use AI · +1.04 pp

compaction roots

compaction, roots, loam, tillage, transpiration, ert

474 abstracts · 2% use AI · -0.66 pp

mortality gpp

mortality, gpp, sar, xgboost, segmentation, peatlands

390 abstracts · 96% use AI · +0.50 pp

nexus governance

nexus, governance, wefe, wef, institutional, multi-objective

579 abstracts · 7% use AI · -0.41 pp

Themes are clusters found by grouping abstracts on their sentence embeddings, so their boundaries are statistical rather than official EGU categories. "Uses AI" comes from a keyword detector validated at precision 0.95 and recall 0.9. Point estimates carry roughly half a point to a point of uncertainty from the clustering. Full method on the main trends page.