Bibliographic record
Abstract
In the mid 1970s, an abrupt change in SST and largescale atmospheric circulation over North Pacific was observed (Trenberth 1990; Trenberth and Hurrell 1994). Following the climate shift, many aspects of El Nino notably changed (Wang 1995; Gu and Philander 1995; Wang and Wang 1996; An and Wang 2000). These changes were accompanied by a notable modification in the evolution pattern and spatial structure of the coupled ocean-atmospheric anomalies (Wallace et al. 1998). ENSO prediction skills of the coupled ocean-atmosphere models also exhibit some decadal dependence (Balmaseda et al. 1995; Kirtman and Schopf 1998). Despite a number of hypotheses that have been recently proposed to explain the origin of the decadal variability in ENSO behavior (Gu and Philander 1997; Zhang et al. 1998; Kleeman et al. 1999; Barnett et al. 1999; Pierce et al. 2000), what caused the interdecadal changes of El Nino manner is a still subject of debate. So far, linear methods are used to extract the couple mode from the climate data (Bretherton et al. 1992). A joint singular value decomposition (JSVD) was used to extract the dominant patterns derived for the 1961-75 and 1981-95 periods, respectively (Wang and An 2001). The maximum SST gradient and strongest zonal wind stress anomalies were all displaced eastward about 15 degrees longitude during 1981-95; similar result was mentioned by An and Wang (2000) using the SVD method. Linear assumption implies that the patterns for the wind stress anomalies and SST anomalies during the warm states are strictly symmetric to those during the cold states and both the westerly and easterly anomalies will have an eastward displacement after 1980. However, the atmosphere-ocean coupled mode could be nonlinear. Recently, nonlinear canonical correlation analysis (NLCCA) method was developed via a neural network (NN) approach (Hsieh 2000). This method has been applied to study the relation between the tropical Pacific sea level pressure (SLP) and sea surface temperature (SST) by Hsieh (2001), where nonlinearity was found in both fields and the nonlinearity exhibited some interdecadal dependence. In this work, NLCCA will be used to study the nonlinear air-sea interactions between the wind stress (WS) and the SST fields over the tropical Pacific at various lead/lag times. Also NLCCA will be applied to investigate the interdecadal changes of the ENSO mode by comparing the NLCCA modes before (1961-75) and after (1981-
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 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".