Estimating resilience for water resources systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Resilience characterizes the recovery capacity of repairable systems from the failure state to the safe state. Resilience has been recognized as a meaningful probabilistic indicator for evaluating risk‐cost trade‐offs in water resources systems. Traditionally, the resilience in the discrete time domain is estimated by sampling methods, which have a high computational expense. No single approximation approach has been well developed for estimating resilience, even under stationary conditions. This paper proposes two practical approximation methods for estimating the lag‐1 resilience in the discrete time domain. Both methods are theoretical developments, one based on a bivariate normal distribution, and the other based on a stochastic linear prediction of the performance function using the mean point of the failure domain. The foundations of both methods are the first‐order reliability method and the periodic vector autoregressive moving‐average time series model. The methods are robust for a wide range of problem characteristics and are applicable for systems facing stationary or nonstationary input conditions.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it