Bibliographic record
Abstract
Decían que había como mil pichis escondidos en la tierra, ¡enterrados! Que tenían de todo: comida, todo. Muchos decían tener ganas de hacerse pichis cada vez que se venían los Harrier soltando cohetes. Rodolfo Foghill Los Pichiciegos (1994) Organisms burrow in response to many biotic and environmental factors. Ichnological studies provide detailed information on environmental parameters involved during sediment deposition and, therefore, serve as a basis for sedimentary environment and facies analysis. To that end, ichnological analysis should focus on the paleoecological aspects of trace-fossil associations (e.g. ethology, feeding strategies, ichnodiversity) and should avoid the simple use of a checklist approach because this may lead to paleoenvironmental misinterpretations. The paleoecological approach needs to be integrated with facies analysis, and should never aim to replace it. Many factors define the niche and survival range of animal species. However, the key to the analysis is the identification of major control factors, which are called limiting factors (Brenchley and Harper, 1998). In this chapter, we revise the response of benthic organisms to different environmental parameters, evaluate the role of taphonomy, and address a set of concepts that should be employed in paleoecological analysis of trace fossils, such as ichnodiversity and ichnodisparity, population strategies, and the notion of resident and colonization ichnofaunas. Then, based on the concept of ecosystem engineering, we discuss how organisms affect the environment. Finally, we address what biogenic structures can tell us about organism–organism interactions and spatial heterogeneity.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".