The new Irish Soil Moisture Observation Network – ISMON: an Umbrella for Integrating Several Recent Soil Moisture Measurements Initiatives
Bibliographic record
Abstract
Real-time soil moisture measurements are essential to manage for adaptive dynamic management of climate change adaptation and reduction of nutrient losses and greenhouse gas emissions from agriculture and forestry. Soil moisture status influences crop growth, run-off, groundwater recharge, land surface-atmospheric exchange dynamics and greenhouse gas emissions as well as the risk of forest fire danger. Here we present the new Irish Soil Moisture Observation Network (ISMON) as an umbrella to bring together several recently established long-term environmental observational networks. These are: 1) initiative of the AGMET group (agmet.ie), 2) COSMOS UK – Northern Ireland, 3) Teagasc NASCO (National Agricultural Soil Carbon Observatory) and 4) Terrain-AI, all of which include several different methodologies for measuring soil moisture at field scale. For instance, AGMET is installing novel cosmic ray neutron sensors which can provide field averaged soil moisture estimates (400m diameter) and Teagasc NASCO and Terrain AI are using Time Domain Reflectometry probes. Such networks are seen as necessary to resolving the problem of scale between point, field-based measurements and satellite-derived soil moisture products and are important in monitoring key biogeochemical processes that vary rapidly in time and space. In the initial phase of the implementation of the ISMON network, the current distribution of the stations in relation to the other networks are presented. The ISMON aims to represent the most relevant soil types, land cover, and regional climate regimes to corroborate direct measurements of soil moisture; it will adapt its design to improve the monitoring network as required. The ISMON will make a valuable contribution to, and expand the international soil moisture monitoring network.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".