Present soils and past land use: the “bracken economy” in Lea-Artibai County (Basque Country, northern Spain) in the late nineteenth and early twentieth centuries
Bibliographic record
Abstract
Soils in Lea-Artibai County (northern Spain) show three significant features: frequent absence of A horizons, higher nutrient concentrations in the surface mineral horizon of past or present arable fields compared to those in forest or shrubland, and the common presence of calcareous horizons in arable fields which is out of character with the region’s humid climate. Farmers stopped applying lime around 1950, so the third feature is interpreted as the result of over-liming since the eighteenth century. The “maize revolution” that began in the mid-seventeenth century relied upon a three-crop rotation system using bracken as a primary fertilizer along with animal manure and lime obtained from local kilns that burned gorse. Extraction of these plant materials resulted in a negative phosphorus balance of phosphorus and the acidification of shrubland soils. The county could not accommodate these various land uses in the early twentieth century, and extraction of leaf litter from forests and shrublands became necessary. In the “concentrational agriculture” of the maize revolution, organic matter and nutrients accumulated in arable fields and diverted ecological pressure onto shrubland and forest soils, creating a “metabolic rift” that is still evident in the soils of Lea-Artibai County.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".