High-resolution multiproxy climatic records from ice cores, tree-rings, corals and documentary sources using eigenvector techniques and maps: assessment of recovered signal and errors
Bibliographic record
Abstract
Palaeotemperature sensitive series from tree-rings, ice cores, corals and documentary sources are combined to producepalaeo summer-temperaturereconstructions (AD 1970-1761)and geographical eigenvector (EOF) maps in both hemispheres. They are compared favourably to those of existing summer-temperature average series. There are 51 palaeoseries in the Northern Hemisphere (mostly north of 40° N) and 16 series in the Southern Hemisphere. The statistics and significance of the palaeoreconstructions are examined by: (1) finding the correlation coefficient (palaeo to measured) as a function of the number and geographical distribution of the palaeo series; (2) developing and running a multiproxy model that generates pseudo series containing a signal and the same types and amounts of noise found in the various real palaeo series. The model reproduces the measured correlation coefficients and the eigenvector's (EOF) explained variances as functions of the number of sites. About 77% of the signal variance can be recovered with 51 well-distributed palaeo series and about 90% with greater than 100 series. The 1st eigenvector (EOF l) component maps in the Northern and Southern Hemispheres are fundamentally different, with the Northern having much less longitudinal variation than the Southern. This means (statistically) that simple hemispheric averages of summer temperature have more meaning in the Northern Hemisphere than in the Southern. Even though the various palaeoseries have different spectral biases, noise types and amounts, they were all used together and the common signal extracted. The results of this work strongly suggest that the multiproxy method is valid and that with enough sites most of the summer signal and geographical pattern can be extracted.
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".