PRISMΔDB
SAE MODEL #2

FEATURE 1302

/ Sensing and Detection Technologies
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This page shows the detailed analysis of a specific feature in the Sparse Autoencoder (SAE). It includes the semantic interpretation generated by the LLM, the top activating documents that trigger this feature, and statistical metrics like density and activation distribution.

Semantic Interpretation
gemma3:12b #7

The positive examples consistently describe devices and methods for sensing, detecting, and analyzing various substances or phenomena. This includes light-emitting devices, hydrophones, microfluidic chips for chemical detection, acoustic localization techniques, biosensors for bacteria detection, nanoscale vibration sensors, and related applications. The core theme revolves around using physical or chemical principles to identify and quantify specific targets or properties. The negative examples, conversely, deal with biological and medical research topics (wortmannin inhibition, cerebellar neuron activity, rodent carcinogenesis, tumor oxygenation) that do not fit this sensing/detection paradigm.

STATISTICS & DISTRIBUTION
Statistics Explained

Density
Fraction of documents where this feature activates at least once.
Higher density = feature appears frequently across the dataset.

Peak Activation
Maximum activation value observed for this feature over all documents.

Activation Histogram
Distribution of all activation values for this feature. Each bar represents a bin (range) of values, and its height shows how many documents fall in that range.

Density
0.01700
Peak Act
3.75
0.0 Max
Global Context
TOP ACTIVATING CONTEXTS
DOC #101 ANALYZE
ACT: 3.7531
Let it shine: a transparent and photoluminescent foldable nanocellulose/quantum dot paper. Exploration of environmentally friendly light-emitting devices with extremely low weight has been a trend in recent decades for modern digital technology. Herein, we describe a simple suction filtration method to develop a transparent and photoluminescent na…
DOC #549 ANALYZE
ACT: 2.7530
DFB fiber laser hydrophone with band-pass response. A distributed-feedback fiber laser hydrophone with band-pass response is presented. The design of the hydrophone aims to equalize static pressure and eliminate signal aliasing of high-frequency acoustic components. Theoretical analysis is presented based on electro-acoustic theory. The experiment…
DOC #455 ANALYZE
ACT: 2.6857
A fully integrated passive microfluidic Lab-on-a-Chip for real-time electrochemical detection of ammonium: Sewage applications. The present work reports on the development of a new generation of Lab-on-a-chip (LOC) to perform in-situ and real-time potentiometric measurements in flowing water. The device consisted of two differentiated parts: a pol…
DOC #829 ANALYZE
ACT: 2.5205
Note: Localization based on estimated source energy homogeneity. Acoustic signal localization is a complex problem with a wide range of industrial and academic applications. Herein, we propose a localization method based on energy attenuation and inverted source amplitude comparison (termed estimated source energy homogeneity, or ESEH). This inver…
DOC #404 ANALYZE
ACT: 2.4921
Label-free visual biosensor based on cascade amplification for the detection of Salmonella. Salmonella is a widely distributed, extremely harmful bacteria, the presence of which requires confirmation via an on-site visual biosensor. In this study, we constructed a label-free, cascade amplification visualization biosensor for the sensitive and rapi…
DOC #489 ANALYZE
ACT: 2.4691
Noncontact rack-pinion-rack device as a differential vibration sensor. We study a nanoscale system composed of one corrugated cylinder (pinion) placed between two corrugated plates (racks). The pinion and racks have no mechanical contact, but are coupled via the lateral Casimir force-one of the most spectacular consequences of quantum fluctuations…
TOPOLOGY
CORRELATIONS
W – Weight-space · similarity between decoder vectors (features that point in similar directions in the embedding space).
D – Data / co-activation · features that tend to fire together on the same documents (co-occurrence in the dataset).