Métodos de fertilización y balance de nutrientes en la agricultura orgánica tradicional de la biorregión mediterránea: Cataluña (España) en la década de 1860
Bibliographic record
Abstract
¿Hasta qué punto eran sostenibles las agriculturas orgánicas tardías en la bioregióndel noroeste del Mediterráneo antes de la revolución verde? ¿Se reponían los nutrientes extraídos del suelo? Ofrecemos aquí las respuestas preliminares a través de la reconstrucción del balance de nutrientes de un municipio catalán hacia 1861-1865. Con una densidad poblacional de 59 habitantes por kilómetro cuadrado, similar a otras áreas rurales del norte de Europa en aquel período, y una densidad de ganado por unidad de tierra de cultivo mucho menor, esa comunidad campesina padecía una gran escasez de estiércol. La brecha se cubría por otras vías, muy intensivas en trabajo, que transferían nutrientes desde las zonas incultas hacia las tierras de cultivo. Los viñedos eran un elemento clave en dicho agroecosistema, dado que requerían pocos nutrientes y proporcionaban el subproducto de la poda. Las áreas de bosque y matorral tambiénproporcionaban cantidades importantes de nutrientes a través de la recolección y empleo de biomasa forestal como fertilizante.AbstractTo what extent were the late organic agricultures in the northwestern Mediterraneanbioregion before the green revolution sustainable? Were the soil nutrients replenished? We offerca preliminary answer to these questions by reconstructing the nutrient balance of a Catalan township circa 1861-1865. With a population density of 59 inhabitants per square kilometer, similar to other rural areas of northern Europe in that period, and a density of livestock per unit of farmland much smaller, the rural community suffered a severe shortage of manure. This gap was covered by other very labor intensive means of transferring nutrients from uncultivated areasinto farmland. Vineyards were a key element in this agro-ecosystem, because they require fewer nutrients and provided the by-product of pruning. Woodland and scrubland areas also provided significant amounts of nutrients through the collection and use of forest biomass as fertilizer.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".