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
The shoreline rate-of-change statistic is calculated from sequential measurements of shoreline position. This statistic implicitly represents the cumulative impact of those processes which have influenced shoreline behavior. Knowledge of the phenomenological relationships between the oceanographic processes and the shoreline's response, however, is not required for the computation and utilization of rate-of-change statistics. In this paper we suggest that an understanding of the processes governing shoreline behavior will greatly aid response-centered analyses. This will be true for numerous applications involving shoreline rate-of-change values, especially those which must determine the persistence of short-term deviations from the long-term shoreline trend. Unfortunately, process-response data from most of the world's coastlines are neither synoptic nor of high resolution. In addition, functional relationships between the processes and responses are difficult to quantify due to the synergistic nature of the shoreline processes. For a 7.4 km reach along the Outer Banks, North Carolina, we demonstrate typical problems associated with identifying the principal causes of shoreline movement in a highly dynamic environment. When viewed over the time spans used in shoreline analyses, which utilize remotely-sensed data (~ 10 to 150 years), the spatial continuity of the processes resulting in shoreline movement is limited to relatively narrow geographic segments along the shore. Thus, a single, long-term process, such as sea-level rise, does not appear to dominate shoreline movement over the 134 year record along the Outer Banks. Instead, relatively long-term trends in shoreline movement correspond to cyclic patterns in storm frequency and intensity, and short-term sea-level adjustments. Other processes affecting local sediment budgets, which can be difficult to quantify, include longshore variations in sediment transport and/or variations in the delivery and storage capacity of sources and sinks over time.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.999 | 0.998 |
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