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Record W4410825866 · doi:10.3897/aca.8.e148193

Cosmic-Ray Neutron Sensing as a multi-purpose technology in eLTER platforms

2025· article· en· W4410825866 on OpenAlexaboutno aff
Enrico Gazzola, Philipp Marr, Alejandra J. Donato, Ricardo Dı́az-Delgado, Nicola Colombo, Mario Gallarate, Stefano Gianessi, Róbert Kanta, Thomas Glade, A. Valero, Fiorella Acquaotta, Michele Freppaz

Bibliographic record

VenueARPHA Conference Abstracts · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsAstronomyNeutronCosmic rayPhysicsAstrophysicsCOSMIC cancer databaseNuclear physics

Abstract

fetched live from OpenAlex

Cosmic-Ray Neutron Sensing (CRNS) has emerged among proximal sensors as a reliable technique for non-invasive Soil Moisture (SM) estimation at a scale much larger than the usual point-scale sensors and at sub-daily resolution (Bogena 2015). The CRNS method is now widely used by research institutions and agencies around the world and nation-wide CRNS soil moisture networks have been established (Andreasen 2017, Bogena 2022). A procedure for proper use and calibration has been published by the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture (IAEA 2017). CRNS is also mentioned as a method for the measurement of Soil Water Content (SOHYD_168 variable) in prime eLTER sites (Zacharias 2024). Another flourishing application of the same technology is the measurement of Snow Water Equivalent (SWE - equivalent amount of liquid water stored in the snowpack), with Électricité de France being the first to deploy a full network on the French Alps and the Pyrenees (Paquet and Laval 2007). A smaller but more dense network has been recently deployed on the mountains of Veneto, Italy, by the Regional Agency for Environmental Protection of Veneto (Valt et al. 2024). CRNS is based on detecting ambient neutrons close to the land surface, generated as a consequence of the flow of high-energy particles from space and strongly absorbed by water molecules. The rate of neutron count rate by a detector placed above ground is correlated to the SM within a volume spanning up to a dozen hectares extension and up to 50 cm depth. In the case of SWE, the neutron count rate by a detector placed at the ground and buried by the snow is correlated to the SWE in the snowpack. Finapp developed a light and safe detector based on a lithium-doped plastic scintillator material, suitable for CRNS applications on field (Gianessi 2024). We report ongoing measurements by Finapp CRNS probes integrated in eLTER monitoring sites in Austria (Landslide Observatory at Hofermühle), Spain (Doñana Biological Reserve - DBR) and Italy (Istituto Angelo Mosso). Between Nov. 2022 and the end of 2023, Finapp probes were installed in the DBR, inside Doñana National Park (SE Spain), an LTSER Platform (eLTER), for a comparative monitoring of different habitats. Fig. 1 shows the measured SM dynamics in 3 different sites within the DBR during the period of co-existence of the 3 probes: Juniper (woodlands on top of stabilized sand dunes), Monte Negro (heathlands in the lowland mantle) and Monte Blanco (xeric shrubland on top of stabilized sand dunes). They show a coherent dynamical response to rain events, characteristic of very sandy soils where water quickly infiltrates towards inferior horizons and feeds the aquifer. The effect of the 2024 dry summer is clearly visible. The region of Lower Austria is highly prone to landslides, therefore a long-term monitoring project on slow-moving landslides was established in 2014 at three different sites (Landslide Observatories) to investigate surface and subsurface dynamics (Marr et al. 2023). This monitoring network is embedded in the Austrian LTSER Platform Eisenwurzen as LTER NoeSLIDE. A CRNS probe was installed in May 2023 at the Landslide Observatory at Hofermühle to investigate the role of SM as a triggering factor for landslides. On Feb. 20th 2024, a field sampling campaign of the SM within the sensor footprint was performed and used as a calibration point. Activities in this site included rover mapping campaigns, in which the probe was unmounted and moved to different points to the purpose of obtaining a mapping of the relative variation of SM across the site. Within the Histalp project precipitation time series, we identify the meteorological station of Waidhofen/Ybby as the most relevant to describe patterns at Hofermühle, which is about 7 km away*2. It shows a mean annual precipitation rate of 1197 mm/a (1896-2021) with the wettest months being June, July and August. Between Sept. 12th and 16th 2024, an exceptional atmospheric pattern led to long-lasting heavy rainfalls in many parts of Austria (BML 2024), which is also reflected in the data provided in Fig. 2. The probe at the Istituto Mosso LTER site (NW Italian Alps, 2901 m a.s.l.) was installed in Dec. 2023 to continuously measure SWE during the snow-covered season 2023/2024. To assess the performance of the probe, the measured data were compared with SWE values collected during targeted field campaigns. Fig. 3 shows the variability of the continuous SWE during the analysed period and the SWE values measured in the field, highlighting the good accuracy of the probe. The probe is currently installed to measure the SWE during the snow-covered season 2024/2025, while more probes are planned to be installed at the site with the goal to measure both SWE and SM (during the snow-free season). The same technology has been applied to notably different scenarios, highlighting the versatility and reliability of CRNS for monitoring the dynamics of water in different forms and under a variety of conditions. We suggest that the spreading use of CRNS probes in monitoring networks will improve the comparability across scales and environments.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.259
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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