cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17654523
title: Preliminary Evidence of Prudential Paralysis in State-of-the-Art Reinforcement Learning vs. Resilience in TUI

description:  

**Dataset Description:**

This dataset presents preliminary empirical evidence from comparisons between Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, and agents inspired by the Theory of Unified Intelligence (TUI) in a simulated risk environment. The analysis suggests that TUI-inspired agents exhibit more stable and less negative Failure Gradient (PGF) metrics when risk scales increase, indicating potential advantages in prudential alignment. However, PPO achieves numerically better rewards in high-risk scenarios by adopting complete inaction (0 tripwires), revealing a form of "prudential paralysis" where optimization leads to inert policies.

**Key Findings:**- **Low Risk (0.5):** PPO maximizes rewards (+371) but frequently triggers risks; TUI balances rewards with prudence (PGF ~ -0.06).- **Medium Risk (1.0-1.5):** PPO survives but stagnates; TUI adapts without collapse.- **High Risk (2.0-3.0):** PPO converges to inaction (-2.85 reward, 0 tripwires); TUI operates continuously (-38.54 reward), paying a "metabolic cost" for active risk management.

**Limitations:** Based on 1 seed per condition and a single simulation environment. TUI agents use heuristic approximations of Constitutive Symbiosis, not full implementation. Negative PGF in both indicates no "perfect alignment," only relative advantages.

**Implications:** Consistent with the hypothesis that RL may require constitutive mechanisms beyond reward maximization for scalable prudential alignment. This exploratory evidence calls for replication with more seeds, environments, and algorithms (e.g., A2C, SAC).

**Files Included:**- `sota_ppo_global_summary.csv`: Aggregated results from PPO vs. TUI comparisons across risk levels.- `fase2_global_summary.csv`: Phase 2 experimental summaries.

**Reproducibility:** Results generated using run_sota_comparison.py from the TUI v4.2 repository (https://github.com/jmrgpr/TUI-v4.1) private for know. Full analysis in analisis_sota_concepto.md.

**Author:** José M. Rivera García  **Date:** November 19, 2025  **License:** Apache 2.0 (code) / CC BY-NC-SA 4.0 (documentation)

https://orcid.org/my-orcid?orcid=0009-0000-3013-725X
DOI: 10.15468/dl.284ah9
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-114.1188 29.99913, -113.9688 29.99913, -113.9688 30.14913, -114.1188 30.14913, -114.1188 29.99913))"
 ]
}

 The dataset includes 35 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046196-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25976/k4i9-sn68
title: Lakelse Watershed Monitoring Program

description: The Lakelse Watershed Stewards Society (LWSS) is a volunteer, non-profit organization formed in 2002. We work collaboratively on issues affecting Lakelse Lake and its surrounding watershed. We work actively with other agencies on projects pertaining to water quality and quantity, sedimentation, mapping, habitat restoration projects, community education, and stewardship.
description: A YSI PRO-DSS probe is used to record data parameters at 1-m depth intervals to bottom at 5 different sites of Lakelse Lake. Parameters collected include: temperature, pH, dissolved oxygen (% and mg/L), conductivity, and turbidity.
DOI: 10.15468/dl.zzqmvj
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.9688 29.99913, -113.8188 29.99913, -113.8188 30.14913, -113.9688 30.14913, -113.9688 29.99913))"
 ]
}

 The dataset includes 9 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046198-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ds9ee3
title: Occurrence Download

description: A dataset containing 767 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bombus rubicundus"
 ]
}

 The dataset includes 767 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046199-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.587hhf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.8188 29.99913, -113.6688 29.99913, -113.6688 30.14913, -113.8188 30.14913, -113.8188 29.99913))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046200-251025141854904/datasets/export for details.
DOI: 10.15468/dl.wbh8e6
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.6688 29.99913, -113.5188 29.99913, -113.5188 30.14913, -113.6688 30.14913, -113.6688 29.99913))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046202-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.znsjxq
title: Occurrence Download

description: A dataset containing 2017 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Stereophyllaceae"
 ]
}

 The dataset includes 2017 records from 69 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046203-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6073/pasta/dd858a8903f9d22ec7f4ef03584f5bd5
title: Anatoxin concentrations, algal assemblages, and water quality data for the South Fork Eel, Salmon, and Russian Rivers in northern California, 2022-2023

description: We collected this data to better understand the timing of peak benthic cyanobacterial mat occurrence (specifically taxa associated with anatoxin production, Microcoleus and Anabaena) and mat anatoxin concentrations in rivers. We sampled in northern California on the South Fork Eel, Salmon, and Russian Rivers biweekly in 2022, and the Salmon River biweekly and South Fork Eel weekly in 2023. During each sampling event, we conducted benthic cover surveys, measured in-situ water quality parameters (temperature, pH, dissolved oxygen, conductivity), and collected surface water samples and targeted cyanobacteria samples. In 2022 on all rivers and in 2023 at the Salmon River, we also collected distributed non-targeted periphyton samples to characterize full-reach community compositions. All sampling was completed in 150-m reaches upstream of sensors recording continuous dissolved oxygen, conductivity, and temperature data. We analyzed surface water samples for nitrate, ammonium, soluble reactive phosphate, total dissolved carbon, and dissolved organic carbon. We also analyzed surface water samples from 2022 for major anions (Cl, SO4, Br) and cations (Na, K, Mg, Ca). Targeted-cyanobacteria and non-target periphyton samples were analyzed for anatoxins (and two other classes of toxins, microcystins and cylindrospermopsins), relative abundance of algal taxa (via microscopy), ash-free dry mass, and chlorophyll-a. To estimate mean river depth within the dissolved oxygen footprint upstream of sensors, we kayaked portions of the river and collected river depth measurements. We also measured discharge at each river excluding the Salmon River (due to high discharge) and completed pebble counts at the South Fork Eel River to obtain sediment grain size distributions. Lastly, we estimated daily reach-scale river metabolism (gross primary productivity and ecosystem respiration) using data from dissolved oxygen sensors with the "streamMetabolizer" package in R at all sensor placements.
DOI: 10.5281/zenodo.17654522
title: Preliminary Evidence of Prudential Paralysis in State-of-the-Art Reinforcement Learning vs. Resilience in TUI

description:  

**Dataset Description:**

This dataset presents preliminary empirical evidence from comparisons between Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, and agents inspired by the Theory of Unified Intelligence (TUI) in a simulated risk environment. The analysis suggests that TUI-inspired agents exhibit more stable and less negative Failure Gradient (PGF) metrics when risk scales increase, indicating potential advantages in prudential alignment. However, PPO achieves numerically better rewards in high-risk scenarios by adopting complete inaction (0 tripwires), revealing a form of "prudential paralysis" where optimization leads to inert policies.

**Key Findings:**- **Low Risk (0.5):** PPO maximizes rewards (+371) but frequently triggers risks; TUI balances rewards with prudence (PGF ~ -0.06).- **Medium Risk (1.0-1.5):** PPO survives but stagnates; TUI adapts without collapse.- **High Risk (2.0-3.0):** PPO converges to inaction (-2.85 reward, 0 tripwires); TUI operates continuously (-38.54 reward), paying a "metabolic cost" for active risk management.

**Limitations:** Based on 1 seed per condition and a single simulation environment. TUI agents use heuristic approximations of Constitutive Symbiosis, not full implementation. Negative PGF in both indicates no "perfect alignment," only relative advantages.

**Implications:** Consistent with the hypothesis that RL may require constitutive mechanisms beyond reward maximization for scalable prudential alignment. This exploratory evidence calls for replication with more seeds, environments, and algorithms (e.g., A2C, SAC).

**Files Included:**- `sota_ppo_global_summary.csv`: Aggregated results from PPO vs. TUI comparisons across risk levels.- `fase2_global_summary.csv`: Phase 2 experimental summaries.

**Reproducibility:** Results generated using run_sota_comparison.py from the TUI v4.2 repository (https://github.com/jmrgpr/TUI-v4.1) private for know. Full analysis in analisis_sota_concepto.md.

**Author:** José M. Rivera García  **Date:** November 19, 2025  **License:** Apache 2.0 (code) / CC BY-NC-SA 4.0 (documentation)

https://orcid.org/my-orcid?orcid=0009-0000-3013-725X
DOI: 10.5281/zenodo.17654593
title: Preliminary Evidence of Prudential Paralysis in State-of-the-Art Reinforcement Learning vs. Resilience in TUI

description:  

**Dataset Description:**

This dataset presents preliminary empirical evidence from comparisons between Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, and agents inspired by the Theory of Unified Intelligence (TUI) in a simulated risk environment. The analysis suggests that TUI-inspired agents exhibit more stable and less negative Failure Gradient (PGF) metrics when risk scales increase, indicating potential advantages in prudential alignment. However, PPO achieves numerically better rewards in high-risk scenarios by adopting complete inaction (0 tripwires), revealing a form of "prudential paralysis" where optimization leads to inert policies.

**Key Findings:**- **Low Risk (0.5):** PPO maximizes rewards (+371) but frequently triggers risks; TUI balances rewards with prudence (PGF ~ -0.06).- **Medium Risk (1.0-1.5):** PPO survives but stagnates; TUI adapts without collapse.- **High Risk (2.0-3.0):** PPO converges to inaction (-2.85 reward, 0 tripwires); TUI operates continuously (-38.54 reward), paying a "metabolic cost" for active risk management.

**Limitations:** Based on 1 seed per condition and a single simulation environment. TUI agents use heuristic approximations of Constitutive Symbiosis, not full implementation. Negative PGF in both indicates no "perfect alignment," only relative advantages.

**Implications:** Consistent with the hypothesis that RL may require constitutive mechanisms beyond reward maximization for scalable prudential alignment. This exploratory evidence calls for replication with more seeds, environments, and algorithms (e.g., A2C, SAC).

**Files Included:**- `sota_ppo_global_summary.csv`: Aggregated results from PPO vs. TUI comparisons across risk levels.- `fase2_global_summary.csv`: Phase 2 experimental summaries.

**Reproducibility:** Results generated using run_sota_comparison.py from the TUI v4.2 repository (https://github.com/jmrgpr/TUI-v4.1) private for know. Full analysis in analisis_sota_concepto.md.

**Author:** José M. Rivera García  **Date:** November 19, 2025  **License:** Apache 2.0 (code) / CC BY-NC-SA 4.0 (documentation)

https://orcid.org/my-orcid?orcid=0009-0000-3013-725X
DOI: 10.15468/dl.yct2ww
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.5188 29.99913, -113.3688 29.99913, -113.3688 30.14913, -113.5188 30.14913, -113.5188 29.99913))"
 ]
}

 The dataset includes 65 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046204-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p97epu2f
title: Thermal Data and Navigation for T-3 (Fletcher's) Ice Island Arctic Ocean Heat Flow Studies, 1963-73 (ver. 1.1 December 2022)

description: The T-3 (Fletcher's) Ice Island in the Arctic Ocean was the site of a scientific research station re-established by the Naval Arctic Research Laboratory starting in 1962. Starting in 1963, the USGS acquired marine heat flow data and coincident sediment cores at sites in Canada Basin, Nautilus Basin, Mendeleev Ridge, and Alpha Ridge as the ice island drifted in the Amerasian Basin. At least 584 heat flow penetrations were attempted, and data were reported at 356 of these. This dataset is the enhanced version of the original data table from Lachenbruch and others (2019; see cross-reference), incorporating additional information such as the probable dates of measurement, physiographic province and surficial geology at each station, and the length of recovered cores. In addition, this dataset converts the original units to SI units and the positions to decimal degrees.  Reference cited: Lachenbruch, A.H., Marshall, B.V., and Ruppel, C.D., 2019, Post-expedition report for USGS T-3 Ice Island heat flow measurements in the High Arctic Ocean, 1963-1973: U.S. Geological Survey data release, https://doi.org/10.5066/P91XQ3IS.
DOI: 10.15468/dl.ha3fsf
title: Occurrence Download

description: A dataset containing 67 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.3688 29.99913, -113.2188 29.99913, -113.2188 30.14913, -113.3688 30.14913, -113.3688 29.99913))"
 ]
}

 The dataset includes 67 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046205-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p13lyabx
title: Volatile organic compounds in passive soil-gas samples at the Bandera Road Ground Water Plume Superfund site, Leon Valley, Bexar County, Texas, June 2025

description: The Bandera Road Ground Water Plume Superfund site (hereinafter referred to as "the site") is located in the City of Leon Valley, Bexar County, Texas, and is in a commercial and industrial area of Leon Valley with nearby residential areas. The site is currently comprised of two discontinuous areas of investigation (AOIs); the second of which (AOI 2) contains a former operation dry-cleaning facility and is the focus of the investigation described herein. The detection of target compounds, including tetrachloroethene, in the shallow soils under the dry-cleaning facility suggest that the dry-cleaning equipment was likely the source of contamination. In 1995, groundwater, soil, and soil-gas samples were collected in the vicinity of AOI 2 in conjunction with a remedial investigation of the site, and both the soil and soil-gas samples contained tetrachloroethene. It is unknown if vapor migration from contaminated soil and groundwater is still occurring or if the site poses a vapor intrusion concern for residences and businesses adjacent to or at the site.

To evaluate the status of soil-gas quality, the U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (EPA), deployed and retrieved soil-gas samples from 41 discrete locations near a church, several small businesses, and private residences located approximately 150 to 1,000 feet southwest of AOI 2 at the site in June 2025. This dataset includes soil-gas sample locations and environmental and quality-control soil-gas data sampling results for a suite of volatile organic compounds at the site. A field trip and data summary document describing USGS data-collection activities and summaries of the associated soil-gas-quality data is also provided with this data release.
DOI: 10.5281/zenodo.17654600
title: RESEARCH QUESTIONNAIRE
DOI: 10.5281/zenodo.17654601
title: RESEARCH QUESTIONNAIRE
DOI: 10.6084/m9.figshare.30049264
title: Dataset and Codes associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

description: <b>Description of the dataset</b><br>This dataset is associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches.”</i> It provides openly accessible monthly climatic data for three representative forest ecosystems in northeastern Algeria: Yabous, Messara, and El Hamma, covering the period 1993–2023 (31 years). The Excel file is organized into five sheets containing monthly averages of key climatic variables: precipitation (mm), mean temperature (°C), minimum temperature (°C), maximum temperature (°C), and wind speed (m/s).<b>Methods</b><br>Climatic data were obtained from the Climate Engine platform (https://www.climateengine.org/) and aggregated at the monthly scale to ensure consistency with drought index calculations. These variables represent primary drivers of drought dynamics in semi-arid forest ecosystems. The dataset is intended to support reproducibility of analyses in the associated article and may serve as a baseline for future studies on drought monitoring, climate variability, and ecosystem resilience in North Africa.<b>File format</b><br>Excel file (.xlsx) with five sheets corresponding to each climatic variable.
DOI: 10.6084/m9.figshare.30049264.v1
title: Dataset associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

description: <b>Description of the dataset</b><br>This dataset is associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches.”</i> It provides openly accessible monthly climatic data for three representative forest ecosystems in northeastern Algeria: Yabous, Messara, and El Hamma, covering the period 1993–2023 (31 years). The Excel file is organized into five sheets containing monthly averages of key climatic variables: precipitation (mm), mean temperature (°C), minimum temperature (°C), maximum temperature (°C), and wind speed (m/s).<b>Methods</b><br>Climatic data were obtained from the Climate Engine platform (https://www.climateengine.org/) and aggregated at the monthly scale to ensure consistency with drought index calculations. These variables represent primary drivers of drought dynamics in semi-arid forest ecosystems. The dataset is intended to support reproducibility of analyses in the associated article and may serve as a baseline for future studies on drought monitoring, climate variability, and ecosystem resilience in North Africa.<b>File format</b><br>Excel file (.xlsx) with five sheets corresponding to each climatic variable.
DOI: 10.6084/m9.figshare.30049264.v2
title: Dataset and Codes associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

description: <b>Description of the dataset</b><br>This dataset is associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches.”</i> It provides openly accessible monthly climatic data for three representative forest ecosystems in northeastern Algeria: Yabous, Messara, and El Hamma, covering the period 1993–2023 (31 years). The Excel file is organized into five sheets containing monthly averages of key climatic variables: precipitation (mm), mean temperature (°C), minimum temperature (°C), maximum temperature (°C), and wind speed (m/s).<b>Methods</b><br>Climatic data were obtained from the Climate Engine platform (https://www.climateengine.org/) and aggregated at the monthly scale to ensure consistency with drought index calculations. These variables represent primary drivers of drought dynamics in semi-arid forest ecosystems. The dataset is intended to support reproducibility of analyses in the associated article and may serve as a baseline for future studies on drought monitoring, climate variability, and ecosystem resilience in North Africa.<b>File format</b><br>Excel file (.xlsx) with five sheets corresponding to each climatic variable.
DOI: 10.60520/ieda/114216
title: Concord EPMA_Glass_METHOD_6.5

description: Analytical method description file: This method was designed primarily for analysis of volcanic glasses (e.g. tephra and obsidians) by EPMA (electron probe microanalysis) using the ARL-SEMQ electron microprobe at Concord University. This descriptor utilizes the tephra community EPMA-SEM method template, an implementation of the Community-based Recommendations on Collection, Analysis and Correlation of Tephra Data. See also https://earthchem.org/communities/tephra/
DOI: 10.15468/dl.6mbyt4
title: Occurrence Download

description: A dataset containing 4195 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Danaus plexippus plexippus",
 "Year 2024-2025"
 ]
}

 The dataset includes 4195 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046206-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13796852
title: Indonesian Hoax News Dataset
DOI: 10.15468/dl.qw6zz7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-113.2188 29.99913, -113.0688 29.99913, -113.0688 30.14913, -113.2188 30.14913, -113.2188 29.99913))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046209-251025141854904/datasets/export for details.