RECESIÓN. Ichnology: organism-substrate interactions in space and time
Bibliographic record
Abstract
Los autores, Luis Buatois y Gabriela Mángano, son dos de los más destacados y referentes especialistas en icnología del panorama actual. Se doctoran en la Universidad de Buenos Aires para posteriormente realizar un posdoctorado en la Universidad de Kansas. A posteriori, tras pasar unos años en el Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) de Argentina se desplazan a Canadá donde continúan desarrollando su actividad docente e investigadora en la Universidad de Saskatchewan. La distribución temática del libro, de 358 páginas y 222 figuras, se realiza en tres grandes partes (I) Herramientas conceptuales y Métodos (Conceptual tools and methods), (II) Tendencias espaciales (Spatial trends) y (III). Una Cuestión de Tiempo (A matter of time), cuyo contenido se divide en tres partes. En este libro se hace un tratamiento exhaustivo, meticuloso, actualizado y muy bien documentado de las diferentes ideas y controversias en icnología, desde los conceptos básicos hasta cuestiones muy novedosas y todavía en desarrollo. Se debe considerar un libro de texto que merece la pena utilizar tanto en cursos de icnología, como referencia para paleontólogos y sedimentólogos que quieran saber más sobre esta disciplina por tratar todos los temas y hacerlo de manera ordenada; así como una obra de consulta obligada para icnólogos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".